Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
644
datasets available to search
ShareScore release 0.9.0
Dataset results
644 results for “data visualization”
Figure 7. from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Figure 7. - Dashboard charts summarizing content from species-rank treatments published in open access articles in Zootaxa and Biodiversity Data Journal containing treatments on spiders, filtered to show only specimens collected in Russia (Suppl. material 11). Russia was the country associated with the largest number of specimens in this body of literature. All content shown here is from treatments published in Zootaxa; no specimens collected in Russia were cited in Biodiversity Data Journal treatments on spiders.
Figure 5b. from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Figure 5b. - Dashboard charts summarizing content from 37 open access articles published in Zootaxa and five articles published in Biodiversity Data Journal containing treatments on spiders (Suppl. materials 8, 9). These charts illustrate interoperability of data from XML-based publishing and subsequently marked up legacy literature.Figure 5a.All treatments regardless of taxonomic rank.Figure 5b.Species-rank treatments. <br> Species-rank treatments.
Figure 6. from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Figure 6. - Dashboard charts summarizing content from species-rank treatments published in open access articles in Zootaxa and Biodiversity Data Journal containing treatments on spiders, filtered to show only specimens from the collection of the California Academy of Sciences (Suppl. material 10). CAS was the institution associated with the largest number of specimens in this body of literature. All content shown here is from treatments published in Zootaxa; no CAS specimens were cited in Biodiversity Data Journal treatments on spiders.
Figure 8. from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Figure 8. - Dashboard charts summarizing content from species-rank treatments published in open access articles in Zootaxa and Biodiversity Data Journal containing treatments on spiders, filtered to show only specimens collected by Y. M. Marusik (Suppl. material 12). Marusik was the collector associated with the largest number of specimens in this body of literature. Note that this count excludes specimens that Marusik collected collaboratively with others (see Discussion: Tracking Individuals). All content shown here is from articles published in Zootaxa; no specimens collected by Marusik were cited in Biodiversity Data Journal treatments on spiders.
Figure 5a. from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Figure 5a. - Dashboard charts summarizing content from 37 open access articles published in Zootaxa and five articles published in Biodiversity Data Journal containing treatments on spiders (Suppl. materials 8, 9). These charts illustrate interoperability of data from XML-based publishing and subsequently marked up legacy literature.Figure 5a.All treatments regardless of taxonomic rank.Figure 5b.Species-rank treatments. <br> All treatments regardless of taxonomic rank.
Figure 4b. from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Figure 4b. - Dashboard charts summarizing content from five articles published in Biodiversity Data Journal containing treatments on spiders (Suppl. materials 6, 7). Data elements were XML encoded as part of the routine publication process.Figure 4a.All treatments regardless of taxonomic rank.Figure 4b.Species-rank treatments. <br> Species-rank treatments.
Figure 3a. from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Figure 3a. - Legacy literature dashboard: charts summarizing content from 37 open access articles published in Zootaxa containing treatments on spiders (Suppl. materials 4, 5). Data elements were XML encoded using GoldenGATE.Figure 3a.All treatments regardless of taxonomic rank.Figure 3b.Species-rank treatments. <br> All treatments regardless of taxonomic rank.
Figure 2b. from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Figure 2b. - Number of publications (a; Suppl. material 2) and treatments (b; Suppl. material 3) contributing to spider taxonomy sorted by number of treatments per publication source (e.g., journal, book publisher). Source: the World Spider Catalog, accessed 14 October 2014.Figure 2a.12,377 publications listed in the 2014 World Spider Catalog. Zootaxa is the top ranking venue with 509 titles representing just over 4% of spider taxonomy.Figure 2b.The 2014 World Spider Catalog refers to 126,621 treatments. With 3314 treatments (2.6%), Zootaxa is the third ranking all time venue behind two museum monograph series: Bulletin of the American Museum of Natural History (4537 treatments, 3.6%) and Harvard's Bulletin of the Museum of Comparative Zoology (3761 treatments, 3.0%). <br> The 2014 World Spider Catalog refers to 126,621 treatments. With 3314 treatments (2.6%), Zootaxa is the third ranking all time venue behind two museum monograph series: Bulletin of the American Museum of Natural History (4537 treatments, 3.6%) and Harvard's Bulletin of the Museum of Comparative Zoology (3761 treatments, 3.0%).
Figure 2a. from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Figure 2a. - Number of publications (a; Suppl. material 2) and treatments (b; Suppl. material 3) contributing to spider taxonomy sorted by number of treatments per publication source (e.g., journal, book publisher). Source: the World Spider Catalog, accessed 14 October 2014.Figure 2a.12,377 publications listed in the 2014 World Spider Catalog. Zootaxa is the top ranking venue with 509 titles representing just over 4% of spider taxonomy.Figure 2b.The 2014 World Spider Catalog refers to 126,621 treatments. With 3314 treatments (2.6%), Zootaxa is the third ranking all time venue behind two museum monograph series: Bulletin of the American Museum of Natural History (4537 treatments, 3.6%) and Harvard's Bulletin of the Museum of Comparative Zoology (3761 treatments, 3.0%). <br> 12,377 publications listed in the 2014 World Spider Catalog. Zootaxa is the top ranking venue with 509 titles representing just over 4% of spider taxonomy.
Figure 1a. from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Figure 1a. - GBIF records proportioned by selected taxonomic groups (Suppl. material 1). Inner ring shows vertebrates, insects, arachnids, other animals, plants, and other kingdoms; outer ring separates birds from other vertebrates, Hymenoptera from other insects, and spiders from other arachnids.Figure 1a.All records in GBIF (n = 517,325,595).Figure 1b.Specimen-based records in GBIF (n = 98,144,242). <br> All records in GBIF (n = 517,325,595).
Figure 3b. from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Figure 3b. - Legacy literature dashboard: charts summarizing content from 37 open access articles published in Zootaxa containing treatments on spiders (Suppl. materials 4, 5). Data elements were XML encoded using GoldenGATE.Figure 3a.All treatments regardless of taxonomic rank.Figure 3b.Species-rank treatments. <br> Species-rank treatments.
Figure 1b. from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Figure 1b. - GBIF records proportioned by selected taxonomic groups (Suppl. material 1). Inner ring shows vertebrates, insects, arachnids, other animals, plants, and other kingdoms; outer ring separates birds from other vertebrates, Hymenoptera from other insects, and spiders from other arachnids.Figure 1a.All records in GBIF (n = 517,325,595).Figure 1b.Specimen-based records in GBIF (n = 98,144,242). <br> Specimen-based records in GBIF (n = 98,144,242).
Supplementary material 15: Species dashboard: Tenuiphantes tenuis from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts showing only specimens of the linyphiid spider Tenuiphantes tenuis. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 interactive dashboard charts.
Supplementary material 7: Prospective publishing dashboard: species-rank treatments from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts summarizing content from 5 articles published in Biodiversity Data Journal containing treatments on spiders. This page shows data from species-rank treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 dashboard charts.
Supplementary material 14: Treatment dashboard: content from Pardosa zyuzini treatment in Kronestedt and Marusik (2011) from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts showing content from one treatment: Pardosa zyuzini in Kronestedt and Marusik (2011). When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 interactive dashboard charts.
Supplementary material 13: Article dashboard: content from Kronestedt and Marusik (2011) from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts showing content from one article, Kronestedt and Marusik 2011. This page shows data from all treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 interactive dashboard charts.
Supplementary material 6: Prospective publishing dashboard: all treatments from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Dashboard charts summarizing content from 5 articles published in Biodiversity Data Journal containing treatments on spiders. This page shows data from all treatments. When viewed using a browser (such as Google Chrome) with an internet connection, this page sends a series of queries to Plazi and integrates the results with the Google Charts API to produce 37 dashboard charts.
Figure 4a. from: Integrating and visualizing primary data from prospective and legacy taxonomic literature - Biodiversity Data Journal 3: e5063 (12 May 2015) https://doi.org/10.3897/BDJ.3.e5063
Figure 4a. - Dashboard charts summarizing content from five articles published in Biodiversity Data Journal containing treatments on spiders (Suppl. materials 6, 7). Data elements were XML encoded as part of the routine publication process.Figure 4a.All treatments regardless of taxonomic rank.Figure 4b.Species-rank treatments. <br> All treatments regardless of taxonomic rank.
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>
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>
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.