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Fig. A1 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. A1. Box and jitter plot of shape SDM estimated using randomized subsampling experiments in yellow-bellied marmots (whiskers in this figure mark the range from minimum to maximum, with no assessment of outliers, which are irrelevant in the context of this didactic example). The total female sample (F) is split in progressively smaller, mutually exclusive, random subsamples and the same is done for males (M). The first subsample of 70 individuals per sex is almost the same of the total samples; the second consists of two female and two male subsamples with 35 individuals each; the third set of subsamples is made of 17–18 individuals per sex and the fourth and fifth include only 10 or 5 individuals respectively. The vertical axis shows the Procrustes distance between means of females and means of males of each set of subsamples. The red circles show the observed mean female to male Procrustes distance in VIM, Alaskan (bro) and Olympic marmots (oly); they are added to the box and jitter plots of yellow-bellied marmot subsamples whose N is closer to the observed N is these three species.
Fig. 5 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. 5 (next page). Examples of ME. a. Suspected bias due to a time lag in the data collection. The scatterplots (a2-a3) are ordinations of 3D cranial shapes in a large sample of African adult men; above the ordinations (a1), the mean shape differences between green (first round of data collection, N = 377) and blue (second round, N = 161) groups are shown (magnified five times) in dorsal, side and frontal views. In the PCA (a2) there is a small amount of separation between 'green' and 'blue' on PC2 (variance explained in parentheses). In the DA (a3), the 'green-blue' separation on the horizontal axis (DF1, with, in parentheses, the between group variance explained) is almost perfect despite the fact that groups, whose differences are being maximized in this plot, are in fact the real 32 geographic populations (shown using convex hulls - see main text). b. PC1-2 scatterplots of adult 3D craniofacial shapes in a European sample of adult women (N = 351, shown in pink) and men (N = 380, in blue). The configuration is smaller and different from the one in (a). Above the scatterplots, the shape corresponding to the positive extreme of PC1 (variance explained in parentheses) is shown, in dorsal view, using displacement vectors (b1-b2). The scatterplot to the left (b3) is the full landmark configuration, whereas the one to the right (b4) has euryon removed. Euryon dominates PC1 differences (b3) in the full configuration (b1). After removing it (b2), not only there is no landmark that dominates variation on PC1 (b4), but also the separation of females and males disappears and (see main text) the shape variance accounted for by sex drops from 6% to 3%. c. PC1-2 scatterplot (variance explained in parentheses) of marmot mandibular shape using the reduced landmark configuration in real (c1) and simulated (c2) samples of hoary marmots and woodchucks. The simulated data are obtained by adding to the original shape coordinates large random gaussian noise (SD = 0.05) before Procrustes re-superimposing the data. Random noise (c2) completely obliterates the real differences (c1) and brings the F ratio and Rsq from F = 38 and Rsq = 20%, in the real data, to F = 2 and Rsq = 0.8% in the simulated ones. In both datasets, the tangent space approximation (assessed in TPSSmall) in the real data (c1) produces a correlation of one between shape distances; however, whereas the slope of the least square regression (tangent space Euclidean shape distances onto Procrustes shape distances) is one in the real data, it is 0.97 in the simulated ones, which suggests an almost problematically large amount of shape variance in this second dataset.
Code and Data for the Study "A User-Centric Model of Connectivity in Street Networks"
<p>This resource contains the code and results used in the paper:</p> <p>Corcoran, P. and R. Lewis (Pending) “A User-Centric Model of Connectivity in Street Networks”</p> <p>Please consult <strong>UserGuide.pdf</strong> for further information. </p>
Nantes-MobileHDRVQA Dataset: Video Quality of User Generated Mobile HDR Videos
<div>Nantes-MobileHDRVQA Dataset contains 60 source videos (SRC), each compressed with AV1 codec at different bitrate and resolution pairs. More info in ReadMe file.</div> <div>A subjective experiment with Absolute Category Rating with Hidden Reference (ARC-HR) protocol was conducted to collect video quality ratings in the range of (1, 5) where higher values indicate better video quality.</div> <div>The experiments were conducted in laboratory conditions at the facilities of Nantes University, France. </div> <div>For each playlists, individual subjective opinion scores and MOS, DMOS, and 95% CI of the MOS is given for each playlists in corresponding playlists</div> <div>Moreover, playlists are combined in the plistall_ACR.csv file with their MOS, DMOS, and 95% CI of the MOS scores. </div>
Efficacy of Transformational Breath® for anxiety management in professional voice users
<p>Raw data for publication of original research entitled "<span>Efficacy of Transformational Breath<sup>®</sup> for anxiety management in professional voice users".</span></p> <p><span>Randomised controlled trial</span></p> <p><span>Quantitative and qualitative data sets relating to responses of treatment and control groups.</span></p>
5G-IANA: UC4 Dataset Mobile users data rate participating in the use case
<p>Location of the mobile user and its nearby place data including their type such as restaurant, café, market, banks and gas station</p>
A Service Robot in the Wild: Analysis of Users Intentions, Robot Behaviors, and Their Impact on the Interaction
<p>This file contains human-robot interaction data acquired during an experiment conducted at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI). The campaign focuses on collecting non-identifying data, such as torso trajectories and the internal state of the system, from people in the proximity of a robot. The study spans three days in two different environments at the University Campus Est in Lugano, Switzerland.</p> <div> <div> <div> <div> <p>The campaign adheres to ethical guidelines and is approved by SUPSI's local ethics committee.</p> <p>Duration: Total of 5 hours and 7 minutes.</p> <p>Participants: 1777 individuals tracked.</p> <p><strong>Environments:</strong></p> <ul> <li>Entrance to the campus canteen (demographically diverse, including students and staff).</li> <li>Corridor between classrooms (mainly attended by students).</li> </ul> <p><strong>Data Types</strong>:</p> <ul> <li>Robot Sensor: Timestamps, user ID, 3D torso pose in Robot Sensor frame, interaction intention detector output.</li> <li>Environment Sensor: Timestamps, user ID, 3D poses of torso and hands in Environment Sensor frame, 2D torso positions in the sensor’s field of view.</li> <li>Robot State: Currently selected behavior, state (idle or performing an offering motion).</li> </ul> <p><strong>Key Events</strong>:</p> <ul> <li>Pick Motion: User's hand movement within 0.3 meters of the box.</li> <li>Robot Offer: Robot begins an offering motion.</li> <li>Successful Offer: Pick Motion within 6 seconds of a Robot Offer.</li> </ul> </div> </div> </div> </div> <div> <div> <div> </div> </div> </div>
Figure 3. (Top): Illustration of the therapy selection main menu. This enables the user to select one of three options for the therapy. Stimuli sequence selectors; (Bottom): (a) Short distance – complete visual field; (b) Short distance – macular; (c) Middle-long distance.-Design of a Novel Servo-motorized Laser Device for Visual Pathways Diseases Therapy
<p>distance therapies for the prescribed time suggested by the ophthalmologist.<br> Note that the complete visual field therapy stimulates different parts in the entire visual field<br> whereas macular therapy stimulate only a small part of the visual field, only the first 10° of vision<br> range. In contrast, middle-long distance therapies are not developed inside the device; instead the<br> patient must sit watching a wall, where the stimuli will be presented. Figure 3 (Bottom) shows the<br> sequence selectors for the three different cases. The therapist will choose a desired number of<br> sequences according to the results of the examination to each patient; hence it is completely patient<br> dependent.<br> Once the therapist finishes the particular design of the stimuli sequence, the software<br> automatically displays a window where he can save the customized patient-specific details for future<br> use as a text file.</p>
Results of survey to potential users of repositories of European Poetry on the informational needs
<p>This excel file presents the results of a survey to potential final users of digital repertoires of the European Poetry in order to understand their information needs. This file also presents results of the analysis to the survey: answers to the specific needs of the results - in black if the domain model responds to the needs; green if a new element to the domain is added due to the result.</p> <p>This survey was done in the context of the definition of a domain model for the european poetry. A domain model (or data model) is a milestone in the process of development of a metadata application profile.</p> <p>Together with this survey, other efforts were taken in place:</p> <p>1) Analysis of the structure of databases that serve digital repertoires of European Poetry</p> <p>2) Analysis of the information needs of graphical user interphaces of the digital repertoires of European Poetry</p> <p>The map https://goo.gl/O0mqhI presents the repertoires used and the phases of the analysis.</p> <p>This work is framed in a Starting Grant research project Poetry Standardization and Linked Open Data: POSTDATA (ERC-2015-STG-679528), funded by European Research Council (ERC).</p>
WP4 Fablabs.io User Survey
<p>The purpose of this research study is to understand better the Fablabs.io users and how the platform could be improved for them. <a href="https://www.fablabs.io/">Fablabs.io</a> is the online social network of the international Fab Lab community and the current official list of Fab Labs that share same principles, tools, and philosophy around the future of technology and its role in society. This survey/questionnaire asked users about them, their usage of <a href="https://www.fablabs.io/">Fablabs.io</a>, their expectations about it and how Fablabs.io could support the global Fab Lab network and the Maker movement.</p> <p>You can find the questions <a href="http://make-it.io/wordpress/wp-content/uploads/2017/11/WP4_fablabs.io_survey_questions.pdf">here</a>.</p> <p>You can read more in <a href="http://make-it.io/deliverables/d4-1-innovation-action-report/">D4.1 here</a>.</p> <p>See also: <a href="http://make-it.io/open-data-api/">http://make-it.io/open-data-api/</a></p>
The role of temporal cues in voluntary stream segregation for cochlear implant users
<p>Data from "The role of temporal cues in voluntary stream segregation for cochlear implant users" (DOI: 10.1177/2331216518773226)</p> <p>List of variables:</p> <ul> <li>Subject: Listener's ID</li> <li>Electrode: Stimulation electrode.</li> <li>Rate: Stimulation pulse rate of the distractor stream. The target stream was always presented with a pulse rate of 300 pps.</li> <li>ABpairs: Number of AB duplets in the sequence.</li> <li>Hrate: Hit rate</li> <li>FArate: False alarm rate</li> <li>dprime: d' score</li> <li>d_se: Standard error of the d' score</li> <li>IOmodel: 1 for ideal observer model estimates and 0 for listener's d' scores</li> <li>control: 1 for the control (i.e. no distractor) condition</li> </ul> <p><strong>Note: In figure 3 from the paper, there is an error in the listeners' ID. Starting from the top panel, the correct IDs are: L1, L4, L5, L10, L6, L8 and L9. The IDs provided in the Data.txt file are correct.</strong></p>
Data for "Is Stack Overflow in Portuguese attractive for Brazilian Users?"
<p>Data for Botto-Tobar et al. Is Stack Overflow in Portuguese attractive for Brazilian Users?. ICGSE 2018.</p> <p>This data was built based on data dump from Stack Exchange (https://stackexchange.com) website. It contains two separate databases (Stack Overflow in English and Stack Overflow in Portuguese):</p> <ul> <li>Users</li> <li>Posts, decomposed by Answers and Questions</li> <li>Tags</li> <li>PostTags</li> <li>GenderUser</li> <li>UserLocation</li> </ul> <p>For more information, please visit http://www.win.tue.nl/~mbottoto/files/papers/conference_papers/sopt_icgse2018.pdf or write to <em>m.a.botto.tobar@tue.nl</em></p> <p> </p>
Effects of community management on user activity in online communities
<p>Data and code needed to reproduce the results of the paper "Effects of community management on user activity in online communities", available in draft <a href="https://www.overleaf.com/13551865bzgswwbvkpgq#/52358582/">here</a>.</p> <p>Instructions:</p> <ol> <li>Unzip the files.</li> <li>Start with JSON files obtained from calling platform APIs: each dataset consists of one file for posts, one for comments, one for users. In the paper we use two datasets, one referring Edgeryders, the other to Matera 2019.</li> <li>Run them through edgesense (https://github.com/edgeryders/edgesense). Edgesense allows to set the length of the observation period. We set it to 1 week and 1 day for Edgeryders data, and to 1 day for Matera 2019 data. Edgesense stores its results in a file called JSON network.min.json, which we then rename to keep track of the data source and observation length.</li> <li>Launch Jupyter Notebook and run the notebook provided to convert the network.min.json files into CSV flat files, one for each netwrk file</li> <li>Launch Stata and open each flat csv files with it, then save it in Stata format.</li> <li>Use the provided Stata .do scripts to replicate results.</li> </ol> <p>Please note: I use both Stata and Jupyter Notebook interactively, running a block with a few lines of code at a time. Expect to have to change directories, file names etc.</p> <p> </p>
Dataset of confocal microscopy stacks from plant samples - ImageJ SurfCut: a user-friendly, high-throughput pipeline for extracting cell contours from 3D confocal stacks
<p>This data set contains confocal stacks from <em>Arabidopsis thaliana </em><em>35S::GFP-MBD</em> light grown hypocotyl as well as propidium iodide stained cotyledon pavement cells and shoot apical meristem. This is the test dataset for the Fiji macro SurfCut (https://github.com/sverger/SurfCut; 10.5281/zenodo.2635737)</p> <p> </p> <p><strong>Material and methods:</strong></p> <p>Plant material and growth conditions</p> <p><em>Arabidopsis thaliana </em>wild type Col-0 and the microtubule reporter line <em>GFP-MBD</em> (WS-4, (Marc et al. 1998) were used. Seeds were cold treated for 48 hr to synchronize germination. Plants were then grown in a phytotron at 20°C, in a 16 hr light/8 hr dark cycle on solid Murashige and Skoog medium (MS medium, Duchefa, Haarlem, the Netherlands) with 0.8% agar, 1% sucrose, and no vitamin.</p> <p> </p> <p>Confocal microscopy</p> <p>Cell contour staining in the case of PC_PI_Col0_(1-8).tif and SAM_PI_Col-0.tif was performed by staining the cell wall with Propidium Iodide (PI). Plants were immersed in 0.2 mg/ml propidium iodide (PI, Sigma-Aldrich) for 10 min and washed with water prior to imaging. For imaging, samples were either placed on a solid agar medium and immersed in water, or placed between glass slide and coverslip separated by 400 μm spacers to prevent tissue crushing. Images were acquired using a Leica TCS SP8 confocal microscope, equipped with a water immersion objective (HCX IRAPO L 25x/0.95 W). PI excitation was performed using a 552 nm solid-state laser and fluorescence was detected at 600–650 nm. GFP excitation was performed using a 488 nm solid-state laser and fluorescence was detected at 495–535 nm. Stacks of 1024x1024 pixels (pixel size of 0.363 x 0.363 micron) optical section were generated with a Z interval of 0.5 μm.</p> <p> </p> <p><strong>File list:</strong></p> <p>Light grown hypocotyl, <em>GFP-MBD</em> reporter line:</p> <p>- Hypocotyl_GFP-MBD.tif</p> <p>Cotyledon’s pavement cells, PI staining:</p> <p>- PC_PI_Col0_1.tif</p> <p>- PC_PI_Col0_2.tif</p> <p>- PC_PI_Col0_3.tif</p> <p>- PC_PI_Col0_4.tif</p> <p>- PC_PI_Col0_5.tif</p> <p>- PC_PI_Col0_6.tif</p> <p>- PC_PI_Col0_7.tif</p> <p>- PC_PI_Col0_8.tif</p> <p>Shoot apical meristem, PI staining:</p> <p>- SAM_PI_Col-0.tif</p> <p> </p> <p><strong>Reference:</strong></p> <p>Marc, Jan, Cheryl L. Granger, Jennifer Brincat, Deborah D. Fisher, Teh-hui Kao, Andrew G. McCubbin, and Richard J. Cyr. 1998. “A GFP–MAP4 Reporter Gene for Visualizing Cortical Microtubule Rearrangements in Living Epidermal Cells.” <em>The Plant Cell</em> 10 (11): 1927–39. https://doi.org/10.1105/tpc.10.11.1927.</p>
Supporting data for: "Diaphysator: an online application for the exhaustive cartography and user-friendly statistical analysis of long bone diaphyses"
<p>Example of dataset to be used with the R-shiny application “Diaphysator”, composed of right tibiae and femora.</p> <p>These data file have been published in: Lacoste Jeanson, A., Santos, F., Villa, C., Banner, J., & Bruzek, J. (2018). Architecture of the femoral and tibial diaphyses in relation to body mass and composition: Research from whole-body CT. <em>American Journal of Physical Anthropology</em>, 167, 813– 826. doi: <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/ajpa.23713">10.1002/ajpa.23713</a></p> <p>This zip file contains:</p> <ul> <li>an “Information file” in CSV format</li> <li>various data files for human femora and tibiae in CSV format</li> </ul> <p>For all CSV files, the field separator is the comma “,” and the character used for decimal points is the dot “.”</p>
Pilot 1 user-generated-content
<p>The information of this dataset will be provided by the users who will participate in the Gallery visits, and will contribute content participating in the experiments using the CrossCult Pilot 1 app. This content will represent the users’ reinterpretation of the NG collection information, allowing them to reflect on their experiences. This will include data literals from any subgroups of the NG collection created by user input, preferences and searches.</p>
Qualitative Interview Data: Users and therapists perceptions of myoelectric multi-function upper limb prostheses with direct and pattern recognition control
<p>The data uploaded here were collected in 2016/2017 through semi-structured interviews with prosthesis users and hand therapists. Participants were mainly asked about satisfaction with their prosthetic device and about activities which they perform with the prosthesis. Interviews were conducted in Dutch and German language.</p> <p>All interview data are made publicly available, except for data of prosthesis users who were experienced with pattern recognition control (n=4). Due to the small number of these participants, their interview data is only available upon reasonable request to not compromise participant privacy. </p> <p> </p>
Doctoral Thesis Artifact "User-Centered Tool Design for Data-Flow Analysis"
<p>This artifact contains the evaluation data and source code accompanying the doctoral thesis "User-Centered Tool Design for Data-Flow Analysis" by Lisa Nguyen Quang Do. The artifact contains (1) the survey questions and anonymized answers of the surveys conducted during the thesis, (2) the user study questionnaires, results, and test applications of the user studies conducted for the thesis, (3) the source code of the research prototypes and video demonstrations of their interfaces, and (4) the benchmark suites used for the empirical evaluation of those prototypes.</p>
Brain Invaders Solo versus Collaboration: Multi-User P300-based Brain-Computer Interface Dataset (bi2014b)
<p><strong>Summary:</strong></p> <p>This dataset contains electroencephalographic (EEG) recordings of 38 subjects playing in pair to the multi-user version of a visual P300-based Brain-Computer Interface (BCI) named <em>Brain </em><em>Invaders </em>(Congedo et al., 2011). The interface uses the oddball paradigm on a grid of 36 symbols (1 Target, 35 Non-Target) that are flashed pseudo-randomly to elicit a P300 response, an evoked-potential appearing about 300ms after stimulation onset. EEG data were recorded using 32 active wet electrodes per subjects (total: 64 electrodes) during three randomized conditions (Solo1, Solo2, Collaboration). The experiment took place at GIPSA-lab, Grenoble, France, in 2014. A full description of the experiment is available at <a href="https://hal.archives-ouvertes.fr/hal-02173958">https://hal.archives-ouvertes.fr/hal-02173958</a>. Python code for manipulating the data is available at <a href="https://github.com/plcrodrigues/py.BI.EEG.2014b-GIPSA">https://github.com/plcrodrigues/py.BI.EEG.2014b-GIPSA</a>. The ID of this dataset is <em>bi2014b.</em></p> <p> </p> <p><strong>Full description of the experiment and dataset: </strong><a href="https://hal.archives-ouvertes.fr/hal-02173958">https://hal.archives-ouvertes.fr/hal-02173958</a></p> <p> </p> <p><strong><em>Investigators</em>:</strong> Eng. Louis Korczowski, B. Sc. Ekaterina Ostaschenko</p> <p> </p> <p><strong><em>Technical</em></strong> <strong><em>Support</em></strong>: Eng. Anton Andreev, Eng. Grégoire Cattan, Eng. Pedro. L. C. Rodrigues, M. Sc. Violette Gautheret</p> <p> </p> <p><strong><em>Scientific Supervisor:</em></strong> Ph.D. Marco Congedo</p> <p> </p> <p><strong>ID of the dataset: </strong><em>bi2014b</em></p>
Brain Invaders Cooperative versus Competitive: Multi-User P300-based Brain-Computer Interface Dataset (bi2015b)
<p><strong>Summary:</strong></p> <p>This dataset contains electroencephalographic (EEG) recordings of 44 subjects playing in pair to the multi-user version of a visual P300 Brain-Computer Interface (BCI) named <em>Brain </em><em>Invaders</em>. The interface uses the oddball paradigm on a grid of 36 symbols (1 or 2 Target, 35 or 34 Non-Target) that are flashed pseudo-randomly to elicit the P300 response. EEG data were recorded using 32 active wet electrodes per subjects (total: 64 electrodes) during four randomised conditions (Cooperation 1-Target, Cooperation 2-Targets, Competition 1-Target, Competition 2-Targets). The experiment took place at GIPSA-lab, Grenoble, France, in 2015. A full description of the experiment is available at <a href="https://hal.archives-ouvertes.fr/hal-02173913">https://hal.archives-ouvertes.fr/hal-02173913</a>. Python code for manipulating the data is available at <a href="https://github.com/plcrodrigues/py.BI.EEG.2015b-GIPSA">https://github.com/plcrodrigues/py.BI.EEG.2015b-GIPSA</a>. The ID of this dataset is <em>bi2015b.</em></p> <p> </p> <p><strong>Full description of the experiment and dataset: </strong><a href="https://hal.archives-ouvertes.fr/hal-02173913">https://hal.archives-ouvertes.fr/hal-02173913</a></p> <p> </p> <p><strong><em>Investigators</em>:</strong> Eng. Louis Korczowski, B. Sc. Martine Cederhout</p> <p> </p> <p><strong><em>Technical</em></strong> <strong><em>Support</em></strong>: Eng. Anton Andreev, Eng. Grégoire Cattan, Eng. Pedro. L. C. Rodrigues, M. Sc. Violette Gautheret</p> <p> </p> <p><strong><em>Scientific Supervisor:</em></strong> Ph.D. Marco Congedo</p> <p> </p> <p><strong>ID of the dataset: </strong><em>bi2015b</em></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.