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37 results for “User Interface”
Peridigm User Material Interface Dataset
<p>The dataset includes a Fortran user material routine which is used for a reference finite element and peridynamic model of a dogbone.</p> <p>The user material interface allows the simplified use of already existing material routines in the peridynamic framework Peridigm. The interface is based on the Abaqus UMAT definition and allows the integration of these Fortran routines directly into Peridigm. The integration of UMAT routines based on finite elements in Peridigm eliminates the need for parallel development of existing material models from classical continuum mechanics theory. Thus, all developer of material models can utilize both finite element frameworks and Peridynamics. This opens up new possibilities for analysis, verification and comparison. In order to be able to use the material routine, the UMAT file must be precompiled and copied to a specific folder. With this interface many material routines can be reused and applied to progressive failure analysis.</p>
A large EEG database with users' profile information for motor imagery Brain-Computer Interface research
<p><em><strong>Context </strong></em>: <br> We share a large database containing electroencephalographic signals from 87 human participants, with more than 20,800 trials in total representing about 70 hours of recording. It was collected during brain-computer interface (BCI) experiments and organized into 3 datasets (A, B, and C) that were all recorded following the same protocol: right and left hand motor imagery (MI) tasks during one single day session.<br> It includes the performance of the associated BCI users, detailed information about the demographics, personality and cognitive user’s profile, and the experimental instructions and codes (executed in the open-source platform OpenViBE).<br> Such database could prove useful for various studies, including but not limited to: 1) studying the relationships between BCI users' profiles and their BCI performances, 2) studying how EEG signals properties varies for different users' profiles and MI tasks, 3) using the large number of participants to design cross-user BCI machine learning algorithms or 4) incorporating users' profile information into the design of EEG signal classification algorithms.<br> <br> Sixty participants (Dataset A) performed the first experiment, designed in order to investigated the impact of experimenters' and users' gender on MI-BCI user training outcomes, i.e., users performance and experience, (Pillette & al). Twenty one participants (Dataset B) performed the second one, designed to examined the relationship between users' online performance (i.e., classification accuracy) and the characteristics of the chosen user-specific Most Discriminant Frequency Band (MDFB) (Benaroch & al). The only difference between the two experiments lies in the algorithm used to select the MDFB. Dataset C contains 6 additional participants who completed one of the two experiments described above. Physiological signals were measured using a g.USBAmp (g.tec, Austria), sampled at 512 Hz, and processed online using OpenViBE 2.1.0 (Dataset A) & OpenVIBE 2.2.0 (Dataset B). For Dataset C, participants C83 and C85 were collected with OpenViBE 2.1.0 and the remaining 4 participants with OpenViBE 2.2.0. Experiments were recorded at Inria Bordeaux sud-ouest, France.</p> <p><em><strong>Duration</strong> </em>: Each participant's folder is composed of approximately 48 minutes EEG recording. Meaning six 7-minutes runs and a 6-minutes baseline.</p> <p><br> <strong><em>Documents</em></strong><em> </em><br> <em>Instructions</em>: checklist read by experimenters during the experiments.<br> <em>Questionnaires</em>: the Mental Rotation test used, the translation of 4 questionnaires, notably the Demographic and Social information, the Pre and Post-session questionnaires, and the Index of Learning style. English and french version<br> <em>Performance</em>: The online OpenViBE BCI classification performances obtained by each participant are provided for each run, as well as answers to all questionnaires<br> <em>Scenarios/scripts</em> : set of OpenViBE scenarios used to perform each of the steps of the MI-BCI protocol, e.g., acquire training data, calibrate the classifier or run the online MI-BCI</p> <p><strong><em>Database </em></strong>: raw signals<br> Dataset A : N=60 participants<br> Dataset B : N=21 participants<br> Dataset C : N=6 participants<br> <br> The article that expained the database is available here:<br> Dreyer, P., Roc, A., Pillette, L. <em>et al.</em> A large EEG database with users’ profile information for motor imagery brain-computer interface research. <em>Sci Data</em> <strong>10</strong>, 580 (2023).<br> https://doi.org/10.1038/s41597-023-02445-z<br> </p>
The User Interface and Functionality Charts of Erkki Kurenniemi's Electronic Musical Instruments (EKIS)
<p>This spreadsheet includes data related to user interface and functionality charts of Erkki Kurenniemi's electronic musical instruments. Data covers only musical instruments; not studio equipment. The data set produced as a part of the PhD project "User Stories of Erkki Kurenniemi’s Electronic Musical Instruments" by the author. The data is visualized with a video published in https://vimeo.com/375784663</p> <p>PI and contact information: Mikko Ojanen / https://orcid.org/0000-0002-7833-9659</p> <p>The outlining of charts is based on previous research on DMIs, e.g. by</p> <p>Birnbaum, D., Fiebrink, R., Malloch, J., & Wanderley, M. M. Towards a dimension space for musical devices. <em>Proceedings of the 2005 Conference on New Interfaces for Musical Expression, </em>192-195.</p> <p>Magnusson, T. An Epistemic Dimension Space for Musical Devices. <em>Proceedings of the 2010 Conference on New Interfaces for Musical Expression, </em>43-46.</p> <p>Wanderley, Mortensen M. 2002. Evaluation of input devices for musical expression: Borrowing tools from HCI.<em> Computer Music Journal, </em><em>26</em>(3), 62-76.</p>
Supporting information of a study for the definition and evaluation of a graphical user interface for housing co-design
<p>This dataset is from a study that intends to define, prototype and test a graphical user interface for a housing co-design system. To define the requirements of the interface, we conducted interviews with professionals of architecture, urbanism and social sciences areas, as well as with housing cooperatives and inhabitants of these institutions. An interface solution was prototyped, tested and refined. Then we conducted a heuristic evaluation and a summative evaluation. Such evaluations involved the testing of a high-fidelity prototype, to receive feedback from UX/UI experts, potential users (inhabitants) and architects.</p> <p>S1_File refers to the interview protocol used with the three groups of interviewees. We share the English and Portuguese versions of the interviews with professionals and the original (Portuguese) and translated versions of the remaining ones since these were conducted in Portuguese.</p> <p>S2_File is a dataset reporting the results of the interviews. Each question includes the answers given and the identification (anonymized) of the interviewees who responded to that question.</p> <p>S3_File describes the usability issues identified by the experts during the heuristic evaluation of the high-fidelity prototype. The first page organizes the issues by severity (left) and priority (right). The remaining pages have a table for each issue, including rows for problem designation, heuristic violated, problem description, solution proposal, severity degree, and an image of the interface pointing to the referred issue.</p> <p>S4_File refers to the results of the heuristic evaluation. It includes the identification of each issue, which expert (anonymized) identified such issue, and the heuristic it violates, with the sum of the times each heuristic was violated at the end of each column. At the right, a table presents the consolidation of issues, organized by priority, with columns identifying the issue, severity level, frequency, and priority.</p> <p>S5_File is the script given to potential users to experiment with the interface during the summative evaluation. This script guides the user through the tasks to perform since the prototype does not have all the features functioning.</p> <p>S6_File refers to the questionnaires applied during the summative evaluation with inhabitants. It includes a preliminary questionnaire, a Single Ease Question (SEQ) questionnaire, a System Usability Scale (SUS) questionnaire, and a Graphical User Interface (GUI) questionnaire.</p> <p>S7_File refers to the results of the summative evaluation with inhabitants (potential users).</p> <ul> <li>Page A refers to the preliminary questionnaire with demographic information such as age, gender, education, relationship with digital technologies, etc. Each field corresponds with each inhabitant (anonymised) and the sum and percentage. In the middle, a table presents a summary of the consolidation. In the right possible relations are presented. </li> <li>Page B presents the results of the SEQ questionnaire, identifying the ratings each inhabitant (anonymized) gave each task. A summary of such values is at the right. </li> <li>On page C, the result of each rating for the SUS questionnaire given by each inhabitant (anonymized) is shown. At the bottom is the calculation of the SUS score.</li> <li>Page D presents the GUI questionnaire results for each inhabitant (anonymized), with the average and SD identified for each question. A summary of such results is on the right.</li> <li>Page E holds the notes taken by the researchers based on their observations regarding task performance. The information is organized in tables for each step of each task and includes the completeness, attempts, and time taken for each inhabitant (anonymized) to complete such task. Also, the sum, percentage, average, and SD are registered. Next to each task is a table identifying how many participants accomplished the task at the first attempt.</li> <li>Page F refers to the strong and weak aspects identified by the inhabitants. Strong and weak aspects are identified, as well as which inhabitant (anonymized) has identified them. The sum and percentage are also given. At the right, there is a table with the consolidation of results by combining similar answers. </li> </ul> <p>S8_File refers to the results of the discussion with architects after experiencing the interface. Such results relate to the positive and negative aspects that the architects identified in the interface and its usefulness for architecture. The left table identifies the strong and weak aspects that architects (anonymized) identified and the sum and percentage associated with them. The table on the right consolidates such results, with similar responses combined.</p>
Automated Insights Dataset (AID) and User Interface Depth Dataset (UID)
<p>The Automated Insights Dataset (AID) brings metadata from the 200 most downloaded free apps from each of the 32 categories on the Google Play Store, totaling 6400 apps, with information that goes beyond that presented by app stores, also bringing metadata from AppBrain. The User Interface Depth Dataset (UID) brings a high-quality sampling of the AID, and delves into the identification of 7540 components of 50 component types and the capture of 1948 screenshots of the interface of 400 apps. The component set was based on components of Google Material Design and Android Studio.</p> <ul> <li>The datasets can be viewed in the spreadsheets named "Automated Insights Dataset (AID).xlsx" and "User Interface Depth Dataset (UID).xlsx".</li> <li>The "UID - Screenshots.zip" file contains screenshots of the apps present in the UID, organized in folders by app IDs.</li> <li>The "Source code of the developed tools.zip" file contains Python codes and complementary files used to collect the datasets.</li> <li>The "Discarded apps.zip" file contains the apps discarded in the analysis, it presents screenshots of some apps, collected elements and the reasons that led to these apps being discarded.</li> <li>The "Data explanation.zip" file contains graphical representations of the UID components and textual representations of each data present in the UID and AID, allowing a better understanding of the criteria used.</li> </ul>
UIS Log: Synthetic User Interface with Screenshots Log
<p>These data correspond to the set of problems for evaluating the proposal detailed in Martínez-Rojas et al. 2022. The evaluation utilizes a set of synthetic problems that simulate realistic administrative use cases. Each problem includes a UI Log with a synthetic screenshot corresponding to each event, capturing 3 distinct processes (<em>P</em>) marked by varying complexity levels. These levels are defined by the number of activities, the process execution variants, and the visual features influencing decisions between these variants.</p> <p>The implementation of this proposal can be found in the tool available at <a href="https://github.com/RPA-US/screenrpa" target="_new">this GitHub repository</a>, which utilizes the logs of these 3 processes for validation. Here they are described:<br><br></p> <ul> <li><em>P1 Client creation</em>. A process with <strong>5 activities and 2 variants</strong>. The single decision in this process is made based on the existence of an attachment in the reception email.</li> <li><em>P2 Client validation</em>. A process with <strong>7 activities and 2 variants</strong>. The decision is made based on the user’s response to a query.</li> <li><em>P3 Client deletion</em>. A process with <strong>7 activities and 4 variants</strong>. The decisions are made based on two conditions: (1) the existence of pending invoices and (2) the existence of an attachment to justify the payment of the invoices.</li> </ul> <p>These processes all contain a single decision point, although the one in P3 is complex. All processes include</p> <ol> <li>synthetic screen captures for their activities and</li> <li>a sample event log with a single instance for each variant.</li> </ol> <p>To generate the objects for the valuation, we generate event logs of different sizes (|<em>L</em>|) for each of these processes by deriving events from the sample event log. We consider log sizes in the range of {10, 25, 50, 100} events. Note that we consider complete instances in the log and thus, we remove the last instance if it goes beyond |<em>L</em>|.<br>Some of these logs are generated with a balanced number of instances, while others are unbalanced (<em>B</em>?) which present more than 20% of different frequency between the most frequent and less frequent variants. To average the result over a collection of problems, 30 instances are randomly generated for each tuple < <em>P</em>, |<em>L</em>|, <em>B</em>? >.<br>In this dataset there are 3 zips, one for each family. Each family corresponds to a process:</p> <ul> <li><em>Basic </em>corresponds to P1</li> <li><em>Intermediate </em>corresponds to P12</li> <li><em>Advanced </em>corresponds to P3</li> </ul> <p>Within these folders, we find 30 different scenarios (folder), in which the look and feel of the applications present in the screenshots have suffered little variations. Within each of these scenarios, variations are carried out respecting to the data entered in the forms and the images or attachments present in the user interface to generate log instances depending on the characteristics of each process.<br>For each scenario, we find 8 folders with the concrete problem which is defined by Log_size (in {10,25,50,100}) and Balanced (in {Balanced, Unbalanced}). The name of these folders have this format<em>: Family_LogSize_Balanced.</em><br>Inside each problem folder the UI Log and the screen captures can be found.<br><br><strong>References</strong><br><br>Martínez-Rojas, A., Jiménez-Ramírez, A., Enríquez, J. G., & Reijers, H. A. (2022, September). Analyzing variable human actions for robotic process automation. In <em>International Conference on Business Process Management</em> (pp. 75-90). Cham: Springer International Publishing.</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>
3D Virtual Reality vs. 2D Desktop Registration User Interface Comparison
<p>Thank you for visiting this repository, which contains a ZIP file with the data for our Registration User Interface (RUI) 2D Desktop vs. VR user study.</p>
UEyes: Understanding Visual Saliency across User Interface Types
<p>UEyes is a large eye-tracking-based dataset including 62 participants and 1,980 UI screenshots, covering four major UI types: webpage, desktop UI, mobile UI, and poster. </p> <p>Please cite the following paper:</p> <p>UEyes: Understanding Visual Saliency across User Interface Types</p> <p>https://dl.acm.org/doi/10.1145/3544548.3581096</p> <p>Yue Jiang, Luis A. Leiva, Hamed Rezazadegan Tavakoli, Paul R. B. Houssel, Julia Kylmälä, and Antti Oulasvirta. 2023. UEyes: Understanding Visual Saliency across User Interface Types. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI '23). Association for Computing Machinery, New York, NY, USA, Article 285, 1–21. https://doi.org/10.1145/3544548.3581096</p>
Adapting ADCI Windows Desktop Graphical User Interface to ADCI-HT on IBM BG/Q.
<p>Supplementary Figure 1. <strong>Adapting ADCI Windows Desktop Graphical User Interface to ADCI-HT on IBM BG/Q. </strong>Data flow diagram illustrating the steps required to perform metaphase image processing tasks using A) Windows ADCI, and B) BG/Q ADCI (ADCI-HT) software platforms.</p>
User Interface Logs for SmartRPA
<p>This dataset contains the User Interface (UI) logs used for testing SmartRPA in the range of the paper "Reactive Synthesis of Software Robots in RPA from User Interface Logs".</p>
MODELING.VIS: Video Tutorial and Instructions to use the Graphical User Interface Toolbox
<p>INTRODUCTION: MODeLING.Vis was designed as an attempt to perform interactive data analyses. Given the software's effectiveness in extracting valuable information from the experimental data presented in this study, the applied methods and principles have been presented together with the analysis of results, and the code has been shared. Note, however, that MODeLING.Vis is not commercial, which constrains efforts behind scientific investigations.</p> <p>HYPOTHESIS: For a better understanding of the Graphical User Interface Toolbox, a demo and user manual of the toolbox should be provided for the convenience of users. The electrophoretic dataset should be published together with the tutorial for ease of access. </p> <p>METHODOLOGY: Creation of a practical video tutorial demonstrating how to download, install, run and operate MODeLING.Vis. Direct access to the electrophoretic dataset (protLabled.xls) is provided. </p>
Supplementary materials to: "SilicH2O: a graphical user interface for processing silicate glass Raman spectra and quantifying H2O"
<p>The dataset contains:</p> <ul> <li>supplementary text and figures as a single pdf</li> <li>Raman and compositional data in two separate excel files</li> </ul> <p> </p>
Weta Robot Autonomous Mission with AgRob4Demeter Supervision and Mobile User Interface
<p>This is a demonstration video of <a href="https://gitlab.inesctec.pt/agrob/agrob4demeter/-/tree/ros-foxy?ref_type=heads">AgRob4Demeter </a>Supervision System during a spraying autonomous mission of <a href="https://scorpion-h2020.eu/weta-robot-from-scorpion-wins-the-if-prize/">Weta </a>Robot, with a mobile user interface to follow the mission execution. </p><p> </p>
Designing a User Interface to Explore Collections of Directly-Follows Graphs for Process Mining Analysis - Supplementary Material
<p>Supplementary Material for paper: "Designing a User Interface to Explore Collections of Directly-Follows Graphs for Process Mining Analysis", submitted to BPMDS 2024</p>
Datasets used for Automated EffortLess MicroED Graphic User Interface (AutoLEI): Tyrosine (12), MOF SU-100 (16), protein MutT homolog 1 (38) and Lysozyme (71)
<p>The <strong>Auto</strong>mated<strong> </strong>Effort<strong>L</strong>ess<strong> </strong>Micro<strong>E</strong>D<strong> </strong>Graphic User<strong> I</strong>nterface<strong> (AutoLEI)</strong> is designed to automatically process and merge batches of rotation electron diffraction datasets using <strong>XDS[1]</strong>. This GUI aims to streamline data processing and minimize the need for manual data processing.</p> <p>The four datasets below are examples used in the Automated EffortLess MicroED Graphic User Interface (AutoLEI) paper.</p> <p>GUI available: https://zenodo.org/records/15206752 </p> <p> </p> <p><strong>A. Data information</strong></p> <p>Dataset 1: Tyrosine (Small molecule), 12 datasets in total</p> <p>Dataset 2: SU-100 (Small molecule), 16 datasets in total</p> <p>Dataset 3: MutT homolog 1 (Macro molecule), 38 datasets in total</p> <p>Dataset 4: Lysozyme (Macro molecule), 71 datasets in total</p> <p> </p> <p><strong>B. Data collection </strong></p> <p><strong>Tyrosine</strong> data was collected with an ASI Timepix hybrid detector installed on a JEOL JEM-2100 (200 kV) microscope equipped with a LaB6 filament. A Gatan 914 cryo-holder is employed to collect data at cryo temperature.</p> <p>Data collection software: Instamatic.</p> <p>Electron Microscopy Center, the Department of Materials and Environmental Chemistry, Stockholm University.</p> <table> <tbody> <tr> <th>Data</th> <th>#Frame</th> <th>Step (°)</th> <th>Start (°)</th> <th>End (°)</th> <th>Rotation axis.(°)</th> <th>WL (Å)</th> <th>Camera_l (mm)</th> <th>Size1</th> <th>Size2</th> <th>Pixel Size (1/nm)</th> </tr> </tbody> <tbody> <tr> <td>data1</td> <td>415</td> <td>0.233</td> <td>-50.82</td> <td>45.44</td> <td>129.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data2</td> <td>475</td> <td>0.233</td> <td>-57.19</td> <td>53.07</td> <td>129.4</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data3</td> <td>139</td> <td>0.232</td> <td>-51.23</td> <td>-19.17</td> <td>128.6</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data4</td> <td>421</td> <td>0.233</td> <td>-50.67</td> <td>47.01</td> <td>128.9</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data5</td> <td>448</td> <td>0.233</td> <td>-54.51</td> <td>49.43</td> <td>130.1</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data6</td> <td>330</td> <td>0.233</td> <td>-54.97</td> <td>21.54</td> <td>129.1</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data7</td> <td>41</td> <td>0.232</td> <td>-57.39</td> <td>-48.12</td> <td>128.3</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data8</td> <td>459</td> <td>0.233</td> <td>-53.60</td> <td>53.03</td> <td>128.7</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data9</td> <td>430</td> <td>0.233</td> <td>-38.49</td> <td>61.25</td> <td>129.7</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data10</td> <td>509</td> <td>0.233</td> <td>-56.38</td> <td>61.91</td> <td>128.6</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data11</td> <td>353</td> <td>0.223</td> <td>-61.69</td> <td>16.79</td> <td>130.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data12</td> <td>514</td> <td>0.233</td> <td>-52.99</td> <td>66.36</td> <td>129.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> </tbody> </table> <p> </p> <p><strong>SU-100 </strong>data was collected with an ASI Timepix hybrid detector installed on a JEOL JEM-2100 (200 kV) microscope equipped with a LaB6 filament. A Gatan 914 cryo-holder is employed to collect data at cryo temperature.</p> <p>Data collection software: Instamatic.</p> <p>Electron Microscopy Center, the Department of Materials and Environmental Chemistry, Stockholm University.</p> <table> <tbody> <tr> <th>Data</th> <th>#Frame</th> <th>Step (°)</th> <th>Start (°)</th> <th>End (°)</th> <th>Rotation axis.(°)</th> <th>WL (Å)</th> <th>Camera_l (mm)</th> <th>Size1</th> <th>Size2</th> <th>Pixel Size (1/nm)</th> </tr> </tbody> <tbody> <tr> <td>data1</td> <td>248</td> <td>0.233</td> <td>-16.82</td> <td>40.64</td> <td>131.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data2</td> <td>486</td> <td>0.233</td> <td>-55.22</td> <td>57.62</td> <td>129.4</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data3</td> <td>472</td> <td>0.232</td> <td>-51.78</td> <td>57.67</td> <td>129.3</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data4</td> <td>399</td> <td>0.232</td> <td>-56.68</td> <td>35.79</td> <td>129.8</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data5</td> <td>319</td> <td>0.233</td> <td>-15.51</td> <td>58.42</td> <td>126.1</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data6</td> <td>302</td> <td>0.233</td> <td>-50.47</td> <td>19.52</td> <td>128.6</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data7</td> <td>87</td> <td>0.233</td> <td>-50.01</td> <td>-29.93</td> <td>130.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data8</td> <td>349</td> <td>0.232</td> <td>-57.04</td> <td>23.81</td> <td>130.5</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data9</td> <td>475</td> <td>0.233</td> <td>-48.75</td> <td>61.56</td> <td>128.8</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data10</td> <td>422</td> <td>0.233</td> <td>-48.35</td> <td>49.53</td> <td>128.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data11</td> <td>415</td> <td>0.233</td> <td>-57.64</td> <td>38.62</td> <td>129.0</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data12</td> <td>351</td> <td>0.232</td> <td>-54.26</td> <td>26.95</td> <td>128.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data13</td> <td>466</td> <td>0.233</td> <td>-53.95</td> <td>54.18</td> <td>130.2</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data14</td> <td>450</td> <td>0.233</td> <td>-51.83</td> <td>52.56</td> <td>129.0</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data15</td> <td>443</td> <td>0.233</td> <td>-49.31</td> <td>53.57</td> <td>129.0</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> <tr> <td>data16</td> <td>374</td> <td>0.233</td> <td>-39.30</td> <td>47.46</td> <td>129.3</td> <td>0.0251</td> <td>439.48</td> <td>516</td> <td>516</td> <td>49.860</td> </tr> </tbody> </table> <p> </p> <p><strong>MutT homolog 1</strong> data was collected with a CMOS CetaD detector installed on a Titan Krios G3i with an autoloader. </p> <p>Data collection software: EPUD</p> <p>Cryo-EM infrastructure unit, Scilifelab, Stockholm.</p> <table> <tbody> <tr> <th>Rotation axis.(°)</th> <th>WL (Å)</th> <th>Camera_l (mm)</th> <th>Size1</th> <th>Size2</th> <th>Pixel Size (1/nm)</th> </tr> </tbody> <tbody> <tr> <td>-6.0</td> <td>0.019687</td> <td>2487.83</td> <td>2048</td> <td>2048</td> <td>5.717</td> </tr> </tbody> </table> <p> </p> <p><strong>Lysozyme</strong> data was collected with a CMOS CetaD detector installed on a Titan Krios G2 with an autoloader. </p> <p>Data collection software: EPUD</p> <p>Cryo-EM infrastructure unit, Scilifelab, Stockholm.</p> <table> <tbody> <tr> <th>Rotation axis.(°)</th> <th>WL (Å)</th> <th>Camera_l (mm)</th> <th>Size1</th> <th>Size2</th> <th>Pixel Size (1/nm)</th> </tr> <tr> <td>-174.4</td> <td>0.01968</td> <td>1155.0</td> <td>2048</td> <td>2048</td> <td>12.318</td> </tr> </tbody> </table> <p><strong>C. Reference</strong></p> <p>[1] Kabsch. W. “XDS”, <em>ACTA CRYSTALLOGRAPHICA SECTION D</em>, 2010</p>
A Dataset of Thermal images of User Interfaces
<p>Recent advancement in sensor technology facilitates having a thermal camera at a lower price. These cameras have many potential applications but can also be used for malicious purposes, such as capturing user interfaces and retrieving user information from heat traces in the thermal images. This dataset is created during an interactive study investigating the threat of thermal attacks on user interfaces. We adapted the following experimental setup during data collection.</p> <ol> <li>2 camera perspectives- FLIR E8-XT camera placed behind the participant and Optris PI 450i camera placed left of the participant. </li> <li>4 types of input devices- i) smartphone, ii) 3 keyboards- a PBT keyboard, an ABS keyboard, and a metal frame keyboard</li> <li>3 types of user input data (text, email address, password)</li> </ol> <p>In summary, we have collected 1152 images from the FLIR camera and another 1152 images from the Optris camera through an interactive study with 32 participants. For each participant, we captured 36 images (9 types of user input, 4 types of input devices). The created dataset can be used to evaluate the deep learning model developed to prevent thermal imaging attacks. Furthermore, the ground truth user input of text, email address, and passwords are structured along with the corresponding image ID so that the advanced data-driven model can be employed to identify user input and investigate the type of user input that can be easily cracked using machine learning techniques.</p>
User Study Data for "Push or Pinch? Exploring Slider Control Gestures for Touchless User Interfaces"
<p>User study data from the Push or Pinch? paper, published at the NordiCHI 2022 conference.</p>
Frontmatter: Mining Android User Interfaces at Scale
<p>Frontmatter dataset contains user interface models of about 160,000 Android apps.</p>
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.