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1,617 results for “user”
SSHOC Training Material Video 2 - How to exploit the Ethnic and Migrant Minorities Survey Registry as an academic and non-academic user - training video in English, French and Spanish
<p>A training video about how to search for and learn about surveys using the EMM Survey Registry, both as an academic and non-academic user.</p> <p>This video has been produced in English, French, Spanish</p>
Data for research article "Privacy Explanations – A Means to End-User Trust"
<p>Research data for article "<strong>Privacy Explanations – A Means to End-User Trust</strong>". This package includes the survey and its results.</p>
Figure 4: Low Level view designed by pedagogic user
<p>The low level view of the Treasure Hunt metaphor is represented in the<br> ¯gure 4. In the ¯gure 4 it is possible to see how each avatar must click to<br> the object and answer to a question that the system asks. In each session<br> there are also two avatar named Guide and Helper. These avatars do not have<br> interaction with other avatars because they will give support to other avatars<br> involved in the session.<br> 83</p>
Assessing First Time Usability of a Hand Augmentation Device in a Large Sample of Diverse Users
<p>This upload provides additional information and data related to the paper "<span>Assessing First Time Usability of a Hand Augmentation Device in a Large Sample of Diverse Users</span>" (Clode et al., 2024).</p> <p>The study investigated users' first-time usage of the Third Thumb at "The Royal Society Summer Science Exhibition 2022", a large public engagement event held from July 6th to July 10th, 2022, at The Royal Society in London, England. The exhibition attracted more than 6,000 visitors.</p> <p>The Third Thumb is a supernumerary robotic finger. During the event, nearly 600 participants practiced using the Third Thumb in motor tasks aimed at different aspects of motor control. We examined how various demographic factors affected performance.</p>
Survey for online registered users of HistoricGraves platform
<div>This survey is being conducted by Eachtra Archaeological Projects as part of INCULTUM (2021-2024), a tourism-oriented HORIZON2020 funded project. The main goal of this survey was to better understand users and usage of the Historic Graves website and how to improve the visitor experience.</div>
Disentangling Web Search on Debated Topics - User Study Data
<p>Data of an exploratory, open-ended user study (N = 255) to advance knowledge and uncover relations between the different facets of web search on debated topics. We explored the relations between factors inherent to the searcher and search system (user characteristics, exposure bias), search intercations (confirmation bias, position bias, search effort), and post-search epistemic states (attitude change, knowledge gain). This data set contains the following variables for each of the 255 participants: SERP ranking bias, prior knowledge, attitude strength, receptiveness to opposing views, attitude-confirming clicks, click rank deviation, number of clicks, time on SERP, hover depth, attitude change, knowledge gain.</p>
Unveiling Perspectives - user study data
<p>Data collected with a user study (N=198), investigating how search behavior when searching on debated topics is affected by stance labels that indicate the stance of each search result. The data set contains independent (SERP display condition, SERP ranking condition), dependent (clicking diversity), and exploratory variables (number of clicks on neutral/opposing/supporting search results, mean click viewpoint, number of clicks, mean click rank, task completion time, attitude change, topic) .</p> <p> </p>
Fig. 4 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part B): group comparisons
Fig. 4. Ordinations summarizing species variation in shape using the first two axes of (a) a conventional PCA (total variance in parentheses) or (b) those of a bgPCA (between group variance in parentheses).
Fig. 3 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part B): group comparisons
Fig. 3. Visualizations of species by sex interactions using group means.a. Mean CS profile plot. For size, males are on average slightly larger than females, but the difference is small and roughly similar in all species. Thus, lines are approximately parallel in the profile plot. b. Phenogram of mean shapes. In the phenogram, with the exception of the Alaskan marmot (bro) (whose sampling error is huge, having only eight individuals of known sex), female and male means are paired within each species with almost identical shape distances between sexes in each species. The similarity of SDM shape distances provides an information equivalent, in terms of the magnitude of the sex differences, to that of the parallel lines in the CS profile plot (Fig. 3a).
Fig. 7 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part B): group comparisons
Fig. 7. UPGMA phenogram of Procrustes mean shape distances for the random, mutually exclusive, species subsamples. Shape variation (magnified five times, relative to the grand mean of all species) is illustrated using the six species mean shapes (all specimens included) with wireframes and thin-plate spline deformation grids (drawn in Morpheus et al. - Slice 1999) - but equivalent to those made using MorphoJ or the TPS Series).
Fig. 6 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part B): group comparisons
Fig. 6. PC1–PC2 of mean shapes for the random, mutually exclusive, species subsamples. Shape variation (magnified five times) at the opposite extremes of each PC is shown using wireframes, as well as deformation grids and expansion factors computed in PAST using the thin plate spline interpolation. (In these wireframes, unlike those in MorphoJ, the mental foramen is also connected by a line to its neighbouring landmarks, as PAST constrains users to link all landmarks: the difference is, however, minimal and purely visual).
Fig. 5 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part B): group comparisons
Fig. 5. Example of visualization of shape change: hoary marmot SDM illustrated using (a) superimposed shapes (male mean, in black, and grand mean of female and male means, in grey) or separate diagrams for male (b) and female (c) mean shapes. Focusing on the coronoid region, the violet arrow shows the potentially misleading effect of the superimposition, suggesting a backward 'movement' of the tip of the coronoid in males. Separate diagrams (b–c), in contrast, correctly suggest that change happens in the region whose boundary are marked by the landmarks, with the rostral margin of the coronoid becoming longer (red arrow) in males and shorter (blue arrows) in females.
Fig. 9 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part B): group comparisons
Fig. 9. Divergent allometries and their effect on size-corrected shape. (a) PLS1 summarizing allometries (35% of variance in allometric predictions) vs CS. The vertical lines emphasize the scores of speciesspecific predicted allometric shapes for either the smallest mandible of all North American marmots (CS = 56 mm, emphasized with a vertical yellow line and arrows to show the extrapolations of the allometric trajectories to CS = 56 mm) or the mean CS of all species (CS = 77 mm, emphasized with a light grey vertical line). (b1) Scatterplot of bgPC1–2 (percentages of between group shape variance in parentheses) for the size-corrected shapes predicted using species-specific allometries (i.e., separate slopes) and CS = 56 mm as 'common' size. (b2, inset) Scatterplot of bgPC1–2 of size-corrected shapes using independent trajectories (as in b1) and CS = 77 mm: if differences in slopes were negligible, b1 and b2 should be almost identical.
Fig. 1 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part B): group comparisons
Fig. 1. Box and jitter-plots of CS, for each species. a. Separate plots for females, males and unknown individuals. b. Plots with pooled sexes. As in part A, as well as shown in Table 1, species names in all figures are abbreviated using the first three letters of the scientific name (e.g., caligata = cal) and F for female, M for male, and U for individuals of unknown sex.
Fig. 2 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part B): group comparisons
Fig. 2. Visualization of shape SDM in relation to interspecific differences using a bgPCA. In this, and other Figures, percentages of variance in the scatterplots of multivariate shape are shown in parentheses, below the label for the corresponding axis. On bgPC1–2, which together account for almost all between group variance (94%), there is a large overlap between females and males within each species, whereas, between species, the separation is clear.
Fig. 7 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part A): introduction and preliminary analyses
Fig. 7. Example of search for shape outliers (emphasized in red) in yellow-bellied marmots: (a) phenogram of shape; (b1-2) scatterplots of PC1 vs PC3 and PC4 vs PC9 (percentages of variance accounted for by each PC shown in parentheses); (c-d) visualization of individual 193 using displacement vectors for this specimen relative to the sample mean shape (c), as well as its original photograph (d).
Fig. 4 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part A): introduction and preliminary analyses
Fig. 4. Graphical examination of replicability in shape using the reduced 12 landmarks configuration. a. PCA scatterplot (in parentheses the variance accounted for by each PC) with convex hulls for the first (grey) and second (red) duplicate. b. Example of phenogram used to count 'sister duplicates' in the whole sample (the inset zooms in the phenogram to exemplify how duplicates 1 and 2 of each individual, e.g., number 97, a female, or number 81, a male, etc., should cluster in pairs, if ME is smaller than inter-individual differences).
Fig. 1 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part A): introduction and preliminary analyses
Fig. 1. Study flowchart for both preliminary (A) and main (B) analyses. The flowchart can be used as a reminder for the main analytical steps in a taxonomic study using GMM. To the same aim, at the end of part B, I added a checklist (Appendix B). In the flowchart, I have included the power analysis and few other analyses, which are optional (dotted lines). The power analysis is shown here connected to both the preliminary steps and the group comparisons, because it can be either prospective or retrospective. The sensitivity of results to the inclusion or exclusion of the smallest samples is also connected to both preliminary and main analyses, because it can be used at any step in the analysis. Sometimes (e.g., in the discriminant analysis (DA) of shape), if p is large relative to N and dimensionality reduction is needed, one could also assess the sensitivity of results to the inclusions of different number of PCs. I stress that, as discussed in the main text of both parts A and B, if a taxon is known to have a large SDM, which may vary in pattern depending on the species or subspecies, even preliminary analyses (such as those for ME or outlier detection) should probably be run with separate sexes.
Fig. 3. a in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part A): introduction and preliminary analyses
Fig. 3. a. Final configuration, reduced to 12 landmarks after excluding low precision landmarks. b. Graphical examination of size replicability (12 landmarks configuration) using a plot of CS in the second duplicate against CS in the first duplicate.
Fig. 2. Initial configuration with 15 in A practical, step-by-step, guide to taxonomic comparisons using Procrustes geometric morphometrics and user-friendly software (part A): introduction and preliminary analyses
Fig. 2. Initial configuration with 15 landmarks (a) and analysis of absolute per-landmark imprecision (b, c). Figure 2b shows the profile plot for the summary statistics of per-landmark variance in the two digitizations. Figure 2c shows the scatter of landmarks purely due to digitization error (red landmarks mark the mean form, to which the differences between the first and second digitization were added).
ScienceDex guides
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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.