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DATA FOR STUDY OF PRIVACY ATTITUDE OF USERS OF SOCIAL NETWORKING SITES AND THEIR EXPECTATIONS FROM LAW IN INDIA
<p>In the present study, on Indian population a disproportionate, stratified, purposive, convenience mixed sampling technique has been adopted in order to ensure proper representation of all the stakeholders concerned with regard to the issue of data privacy in India among the population of the study. The stratified sampling technique is popularly used for a large size population and where it is desirable to purposively have an adequate representation of the all the sub-groups. It is estimated that there are about 0.2 million Law Enforcement Officers (Directors General of Police to Assistant Sub- Inspector), 2.2 million Legal Professionals, including 21,586 Judges, 1.5 million Academicians, about 5000 Information Assurance and Privacy Experts, and 450 million Internet Users in India, out of which 196 million use SNSs. It may be clarified here that all these stakeholders are not only the users of social networking sites but also Indian citizens who are stakeholders in enactment and implementation of the privacy law as and when it is enacted.</p> <p>Based on the size of the total population, a statistically adequate sample size of 385, having a 95 per cent Confidence Level, 5 per cent Margin of Error (Confidence Interval), 0.5 Standard Deviation, a 1.96 Z-score was calculated.</p> <p> </p> <p> </p> <p> </p>
Towards a Data-Driven Requirements Engineering Approach: Automatic Analysis of User Reviews
<p>6000 French user reviews from three applications on Google Play (Garmin Connect, Huawei Health, Samsung Health) are labelled manually. We selected four labels: rating, bug report, feature request and user experience.</p> <ul> <li><strong>Ratings</strong> are simple text which express the overall evaluation to that app, including praise, criticism, or dissuasion.</li> <li><strong>Bug reports</strong> show the problems that users have met while using the app, like loss of data, crash of app, connection error, etc.</li> <li><strong>Feature requests</strong> reflect the demande of users on new function, new content, new interface, etc.</li> <li>In <strong>user experience</strong>, users describe their experience in relation to the functionality of the app, how does certain functions be helpful.</li> </ul> <p>As we can observe from the following table, that shows examples of labelled user reviews, each review belongs to one or more categories.</p> <table> <tbody> <tr> <th>App</th> <th>Total</th> <th>Rating</th> <th>Bug report</th> <th>Feature request</th> <th>User experience</th> </tr> </tbody> <tbody> <tr> <td>Garmin Connect</td> <td>2000</td> <td>1260</td> <td>757</td> <td>170</td> <td>493</td> </tr> <tr> <td>Huawei Health</td> <td>2000</td> <td>1068</td> <td>819</td> <td>384</td> <td>289</td> </tr> <tr> <td>Samsung Health</td> <td>2000</td> <td>1324</td> <td>491</td> <td>486</td> <td>349</td> </tr> </tbody> </table> <p> </p> <h2>New Dataset</h2> <p>Based on this dataset, we developed a labeled dataset containing 6,000 English and 6,000 French reviews for classification, as well as 1,200 bilingual reviews for clustering. The new dataset has been made publicly available on Zenodo at the following link: <a href="../records/11066414">https://zenodo.org/records/11066414</a></p>
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects - Graph Files
<p><strong>Explanation/Overview:</strong></p> <p>Corresponding graph files of the extracted Zooniverse networks described in D3.3 (can be found here), which are the result of our research that culminated into the publication "Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects", a conference paper for the conference CollabTech 2022: <a href="https://link.springer.com/book/10.1007/978-3-031-20218-6">Collaboration Technologies and Social Computing</a> and published as part of the <a href="https://link.springer.com/bookseries/558">Lecture Notes in Computer Science</a> book series (LNCS,volume 13632) <a href="https://link.springer.com/chapter/10.1007/978-3-031-20218-6_5">here</a>. Usernames have been anonymised.</p> <p>The graph files are in <code>.gexf</code> (graph exchange XML format) and <code>.gml</code> (graph modeling language) formats which can be used by common graph/network-analysis and visualisation tools such as Gephi.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis for possible further examinations of the network structure, involving additional (not yet analysed) features such as the content of the comments etc.</p> <p><strong>Relatedness:</strong></p> <p>The data of the different projects was derived from the forums of 7 Zooniverse projects based on similar discussion board features. The projects are: 'Galaxy Zoo', 'Gravity Spy', 'Seabirdwatch', 'Snapshot Wisconsin', 'Wildwatch Kenya', 'Galaxy Nurseries', 'Penguin Watch'.</p> <p><strong>Content:</strong></p> <p>The dataset contains distinct graph files for each of the analysed projects. For each graph file, there are <em>nodes </em>and <em>edges</em> and their associated attributes (i.e., each edge can have an attribute). For the edges, apart from source and target, we have as attributes:</p> <ul> <li><code>weight</code></li> <li><code>project_title</code></li> <li><code>body </code>(i.e., text)</li> <li><code>created_at</code></li> <li><code>userRoles</code></li> <li><code>discussion_title</code></li> <li><code>discussion_id</code></li> <li><code>user_id</code></li> <li><code>board_title</code></li> <li><code>relation</code></li> <li><code>target_role</code></li> </ul> <p>For the nodes, the attributes are:</p> <ul> <li><code>user_id</code></li> <li><code>userRoles</code></li> <li><code>degree_reply </code>(i.e., degree for the reply relation)</li> <li><code>in_degree_reply</code></li> <li><code>out_degree_reply</code></li> <li><code>degree_comment</code></li> <li><code>in_degree_comment</code></li> <li><code>out_degree_comment</code></li> <li><code>degree_total</code></li> <li><code>in_degree_total</code></li> <li><code>out_degree_total</code></li> <li><code>target_role</code></li> </ul> <p><strong>Grouping:</strong></p> <p>Each graph file represents all the comments for the respective project across its lifespan irrespective of any time slices. Edges represent the comments and users represent the nodes. While the different boards are still contained within the data, all boards occur in the data.</p> <p> </p>
Research data from the two surveys on IoT implementation for Article "User and Professional Aspects for Sustainable Computing Based on the Internet of Things in Europe"
<p>The file includes data collected through two online surveys linked to the article "User and Professional Aspects for Sustainable Computing Based on the nternet of Things in Europe" published by journal Sensors in January 2023:</p> <ul> <li>Survey on factors that inlfuence IoT Adoption by non technical users</li> <li>Survey on recommended profile focused on IoT implementation for two professional roles in the context of Smart Cities (SC) projects: SC engineer and SC technician.</li> </ul>
Immerse Downstream User Toolbox - example data for FerryBox validation
<p>Example data for FerryBox Validation use case within </p> <p><a href="https://github.com/immerse-project/Downstream-Users-Toolbox">immerse-project/Downstream-Users-Toolbox: Analysis and assessment tools from IMMERSE WP8 (github.com)</a></p> <p>containing exemplary temperature and salinity data from a ship attached FerryBox (<a href="https://www.ferrybox.org/">https://www.ferrybox.org/</a>) and according surface fields of the the CMEMS Atlantic - European North West Shelf - Ocean Physics Analysis and Forecast model <a href="https://resources.marine.copernicus.eu/product-detail/NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013/INFORMATION">NORTHWESTSHELF_ANALYSIS_FORECAST_PHY_004_013</a>, the high resolution NNEMO.v4.2_RC Southern North Sea configuration developed at <a href="https://www.hereon.de/">Helmholtz-Zentrum Hereon</a> in the context of IMMERSE, and the German Bight configuration operated at Hereon, using the <a href="http://ccrm.vims.edu/schismweb/">SCHISM unstructured grid moddeling framework</a> for an assesment of the model performance on those variables within the south eastern German Bight on 2018-11-05.</p>
A User Story Dataset for Library and Restaurant Management Systems
<p>A user story dataset generated by participants during user experiments with <a href="https://doi.org/10.5281/zenodo.7529090">SCOUT</a>. </p>
PROTEIN User Questionnaire
<p>This is the user survey (questionnaire) which was used in PROTEIN and the results. The users were asked to evaluate the PROTEIN platform which was made available to them through a mobile app.</p>
Political, economic, and governance attitudes of blockchain users
<p>Survey responses for academic publication "Political, economic, and governance attitudes of blockchain users"</p>
Lost at C: Data from the Security-focused User Study
<p><strong>2022 Study on the security implications of Large Language Model Code Assistants</strong></p> <p>This repository contains the results of the 2022 study described in the Paper: `Lost at C: A User Study on the security implications of Large Language Model Code Assistants` Link: https://arxiv.org/pdf/2208.09727.pdf</p> <p>Here, the overall goal is to determine if users with access to code suggestions via a Large Language Model (OpenAI code-cushman-001) in a GitHub Copilot-like arrangement produce code with a higher incidence rate of security-related bugs than those without any such access. In particular we concern ourselves with low-level memory-related bugs such as those often present in buggy C code.</p> <p>To answer this question, a User Study was conducted (N=58) which had users implement a shopping list in C as a singly-linked list. Half the users had access to a custom Copilot-like extension which generated suggestions according to code-cushman-001, and half had no access or coding hints other than provided by Visual Studio Code's default Intellisense.<br> The study was performed in a controlled environment (a virtualized cloud-based desktop).</p> <p>This task was made deliberately difficult than usual via the specifications: participants had to implement the shopping list according to an unusual API containing a number of pitfalls. They had to implement only the implementation of the specification (i.e. the `list.c` file). Users were provided `list.h` as well as a suite of automated (if basic) tests.</p> <p>For more details, you can see the associated paper.</p> <p>The repository contains the user study data as well as the scripts used for analysis and results reproduction.</p>
Political, economic, and governance attitudes of blockchain users
<p>Survey data on the political, economic, and governance attitudes of blockchain users with accompanying description, visualization, and analysis.</p> <p>The <a href="https://metagov.typeform.com/cryptopolitics">Cryptopolitical Typology Quiz</a> was developed by the <a href="https://metagov.org/">Metagovernance Project</a> to help the crypto community understand its political, economic, and governance beliefs. Survey results were collected from September 27, 2021 through March 4, 2022 and have been published on the <a href="https://airtable.com/shr9LYMni8pBUVD6q/tblvwbt4KFm8MOSUQ">Govbase Airtable database</a>.</p> <p>This repository contains both a CSV export of the relevant results and the Python code used to visualize the distribution of responses, investigate the importance of blockchain affiliation and self-reported political orientation, and assess the validity of our constructed political score and types against any axes or features that emerge from the data.</p> <p>To view the results, check out the two Jupyter notebooks in this repository, paste the links to them into <a href="https://nbviewer.org/">nbviewer</a> for prettier in-browser viewing, or fork this repository and run them yourself!</p> <p>If you are interested in supporting ongoing work on the Cryptopolitics project, please get in touch with <a href="https://github.com/metagov/cryptopolitics-paper/blob/master/josh@metagov.org">josh@metagov.org</a>. To get involved with Metagov, join the Metagov <a href="https://metagov.pubpub.org/">community</a> or <a href="https://opencollective.com/metagov">staff</a>.</p> <p>The version in this release was used to generate the results and figures for a manuscript <a href="https://arxiv.org/abs/2301.02734">published on arXiv</a> and submitted for consideration for journal publication.</p> <p>Last modified on December 13, 2022.</p>
Six User Personas for the Multilingual DH Community
<p>This document contains six fictional user personas. The personas are based on data collected during a status quo survey carried out in June 2020 as part of the “Linguistic and geocultural diversity in digital knowledge infrastructures” thematic group at the Disrupting Digital Monolingualism Symposium hosted by King’s College London. The personas were originally appended to a paper prepared for an alternative session at the DH Unbound 2022 conference (ACH/CSDH-SCHN).</p>
SCA-2023: A two-part dataset for benchmarking the methods of image precompensation for users with refractive errors
<p>The recent practices of demonstrating various static and video images to users by means of digital, processor-controlled, often self-luminous devices (computer monitors, smartphone and tablet screens, etc.) have spurred the development of various methods for improving the perception of such images through their computer processing. In particular, this applies to the task of precompensating images shown to users with various anomalies of refraction of the eyes (e.g. myopia or astigmatism) in situations where they are not equipped with glasses or other corrective devices. Researchers have proposed a considerable number of such precompensation methods, but to this day there has been no way to accurately compare their quality. We propose an original dataset, which we called “SCA-2023”, of images specially designed for this purpose. Its most important feature is the fact that it includes not only a set of ground-truth images for implementing the precompensation transform, but also a separate set of images characterizing specific types and degrees of manifestation of the refractive errors. The benchmarking procedure itself includes applying the precompensation transformation to a certain image from the first part of the dataset, computer simulation of the so-called retinal image (distribution of light on the retina of an imaginary observer) based on the selection of the “distorting eye” from the second part of the dataset, and evaluating the similarity of this image to the ground-truth image, using any of the commonly used similarity metrics for this purpose.</p>
User Reviews of nine Social VR applications
<p>These user reviews are collected from nine social VR applications on two digital distributation platforms (Steam and Oculus). All these are in English.</p> <p>Five social VR applications are from Steam: VRChat, Rec Room, PokerStars VR, Altspace, VR, Sansar.</p> <p>Eight social VR applications are from Oculus: VRChat, Rec Room, Echo VR, PokerStars VR, Real VR Fishing, Poker VR, Altspace VR, vTime VR.</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>
Mapping User Attention: Filtering and Visualizing Relevant UI Components in Screenshots based on Gaze Fixations
<p>These data correspond to the set of problems used for the evaluation of the proposal <em>What Are You Gazing At? An Approach to Use Eye-tracking for Robotic Process Automation.</em></p> <p>Each problem consists of a set of 10 screenshots with the same <em>look and feel</em> but different data values for those values that can be entered/modify by the user. Each problem has its associated gaze fixation data. In each of the problems there is a <em>key UI element</em> that primarily attracts the attention of the user.</p> <p>The evaluation is based on a set of images which resemble realistic screenshots of activities in the administrative domain. More precisely, 5 different set of screenshots (<em>S</em>) are generated, each of them with a different level of complexity. Complexity is measured in terms of the number of UI elements per screenshot. The sets are:</p> <ul> <li><em>S1 Mockup-based email view</em>. Represents the activity of viewing an email to check if it contains an attachment. In this case, the <em>key UI element</em> that receives the attention is the attachment inside the email.</li> <li><em>S2 Mockup-based CRM user details</em>. Represents a user's detail viewing activity within a Client Relationship Management (CRM) platform. The key UI element is the checkbox that indicates if the user has all his invoices paid.</li> <li><em>S3 Real screenshot email view. </em>Analogous to <em>S1</em> but with real screenshots. It represents the activity of viewing an e-mail to check if it contains an attachment. In this case, the key UI element to which attention is paid is the attachment contained in the e-mail.</li> <li><em>S4 Real screenshot CRM user details. </em>Analogous to <em>S2</em> but with real screenshots. It represents a user's detail viewing activity within a CRM platform. The key UI element is the checkbox indicating whether the user has all their invoices paid.</li> <li><em>S5 Real screenshot CRM user details. </em>Represents the split-screen display of two applications. On the left side a pdf viewer, showing a covid vaccination certificate. And on the right side a human resources management system (basic recreation of real system for privacy reasons). In this one the detail of the employee to whom the certificate of the left side corresponds is visualized. These screenshots, having two applications, have two key UI elements. In the pdf viewer it is the name of the certificate holder and in the human resources management system it is the name of the employee whose detail view is being displayed. The activity being carried out is the verification that the covid certificate received corresponds to that of an employee.</li> </ul> <p>Two types of filters based on the gaze fixation data are applied to these sets of screenshots: <em>Pre-filtering</em> and <em>Post-filtering</em>, corresponding to applying the filtering before and after detecting UI components in the screenshots, respectively. The structure of the data packages is divided in two folders <em>input </em>and <em>output</em>. The <em>input </em>folder is organized as follows:</p> <p><strong>input/</strong></p> <ul> <li><strong>screenshots/</strong>: corresponds to the screenshots. The sets of screenshots are easily identifiable, they are named following the pattern: <em>SX_screenshot_DDDD.jpeg</em>. Where <em>X </em>indicates to which of the set of screenshots described in the previous list it belongs, and <em>DDDD</em> represents a unique identifier for each screenshot. Each group consists of 10 screenshots, being 50 in total.</li> <li><strong>fixation.json</strong>: It is a JSON file that contains a <em>key </em>associated with each of the screenshots. For each screenshot, it contains a "fixation_points" key where information about the fixations that have occurred on the screenshot is stored. Here's an example: <pre><code> "S5_screenshot_0050.jpeg": { "fixation_points": { "334.25#497.166666666667": { "#events": 6, "start_index": 33224, "ms_start": 553962.1467, "ms_end": 554061.9899, "duration": 99.8432000001194, "imotions_dispersion": 0.300325967868111, "last_index": 33229, "dispersion": 14.044275227531914 }, "1258.80769230769#507.576923076923": { "#events": 13, "start_index": 33234, "ms_start": 554128.5427, "ms_end": 554345.3595, ...</code></pre> </li> </ul> <p>The <em>output </em>folder is organized in three subfolders, the first one containing the information of the <em>non-filtered</em> screenshots (i.e. without having applied to them any filtering or processing), and the next two with the information resulting from <em>pre-filtering</em> and <em>post-filtering</em>.</p> <p><strong>output/</strong></p> <ul> <li><strong>non-filter/</strong> <ul> <li><strong>borders/</strong>: screenshots with highlighted borders of <strong>all </strong>UI components detected in it.</li> <li><strong>components_json/</strong>: a collection of JSON files with the same name as the screenshot, containing the "img_shape" key with a list of the screen resolution and the number of layers the image has: [1080, 1920, 3], and the "compos" key with a list of <strong>all</strong> UI components representing the Screen Object Model.<br> </li> </ul> </li> <li><strong>pre-filter/</strong> and <strong>post-filter/</strong> <ul> <li><strong>borders/</strong>: screenshots with the borders of the <strong>relevant </strong>UI components. In the case of prefiltering, the detection of components is only performed on the parts of the screenshot that have received attention. In postfiltering, the complete screenshot is shown, with only the borders of the relevant UI components highlighted.</li> <li><strong>components_json/</strong>: a collection of JSON files with the same name as the screenshot is included, containing the following keys: <ul> <li>"img_shape": A list representing the screen resolution and the number of layers in the image, e.g., [1080, 1920, 3].</li> <li>"compos": A list of all UI components representing the Screen Object Model (SOM). During post-filtering, each UI component is augmented with an additional property called "relevant." If this property is set to <em>true</em>, it indicates that the respective UI component has received attention.</li> </ul> </li> <li><strong>(pre)/(post)filter_attention_maps/</strong>: represent the attention maps. In the case of prefiltering, any surface of the screen that has not received attention will be shown in black. In the case of postfiltering, the areas of attention will be shown as red circles, and the UI components whose area intersects with the areas of attention by more than 25% will be shown in yellow.</li> </ul> </li> </ul> <p>In conclusion, the described data package consists of sets of screenshots, accompanied by prefiltering and postfiltering filters using gaze fixation data, enabling the identification of relevant UI components. The organized data packages include input and output folders, where the output folder offers processed screenshots, UI component information, and attention maps. This resource provides valuable insights into user attention and interaction with UI elements on different types of scenarios.</p>
Resources for User Study of Mapeathor
<p>This dataset contains the resources used to carry out the exercise proposed for the Mapeathor study of usability. It is comprised of:</p> <ul> <li>An ontology diagram representing a subset of the <a href="http://vocab.ciudadesabiertas.es/def/comercio/tejido-comercial/index-en.html"><em>Vocabulary for data representation of the local business census and activities licenses</em></a> </li> <li>Three clean CSV files with data associated with the subset ontology: <ul> <li>bar.csv and restaurant.csv: Information about local businesses (bars and restaurants respectively), with the following fields: <em>id, URL, email, longitude, latitude, phone, maximum_capacity, name</em>.</li> <li>address.csv: Information about the postal address of local businesses, with the following fields: <em>id, restaurant_name, postal_code, country, city, address</em>.</li> </ul> </li> </ul>
Reference datasets for consistency tests of GENAPOPOP 1.0 software: a user-friendly software to analyse genetic diversity and structure in partially clonal and selfed polyploid organisms.
<p>Datasets companion of the manuscript entitled GenAPoPop 1.0: a user-friendly software to analyse genetic diversity and structure in partially clonal and selfed polyploid organisms, used to achieve consistency test with Spagedi 1.5 software, and used as reference datasets to demonstrate the new possibilities allowed by GenAPoPop software.</p> <p>Raw datasets used for testing GenAPoPop 1.0, A user-friendly software for easily compute genetic analyses of autopolyploid populations packaged for Linux, MacOS and Windows; Results obtained from Spagedi 1.5 (Hardy & Vekemans 2001) and GenAPoPop1.0.</p> <p>Four pseudo-observed genotyping autotetrapolyploid SNP datasets, corresponding respectively to panmictic (A), highly clonal (B), highly selfed (C) and half-clonal-half-selfed (D) reproductive mode scenario. In all these four scenarios, we simulated two populations of 100 individuals each, connected with a migration rate of 0.01 and mutating at a rate of 0.01, genotyped at 10 SNPs. Datasets were recorded 1000 generations after an initial randomly drawing population with equal allele frequencies.</p> <p>One SNP tetraploid genotyping dataset from two French <em>Ludwigia grandiflora subsp. hexapetala</em> populations (aquatic plant from the Angiosperm clade): two populations in which we collected 75 individuals, each genotyped with 36 SNPs using the Hiplex method allowing confident allele dosage (Delord et al. 2018).</p> <p>One microsatellite tetraploid genotyping dataset on two Aulactinia stella populations (sea-anemone from the Cnidaria phylum), sampled on the coast of the arctic ocean. One population of 21 individuals and one population of 15 individuals, both genotyped with 10 microsatellites.</p> <p>We also report here the consistency tests with GenAlex and Spagedi, results of analyses (GPP tab) on 6300 independant simulations and inferences of the quantitative reproductive modes using the bayesian method on CEMP tab made on 6300 another independant simulations.</p>
VR Gaming Dataset - Multi-user tests
<p>This virtual reality (VR) gaming dataset includes <strong>Wireshark</strong> traces (.csv) and <strong>ALVR</strong> statistics (.json) for <strong>three users</strong> (Costas, Miguel, Daniele) playing at the same time in a <strong>Wi-Fi 6</strong> network with <strong>one access point</strong> (AP). We used multiple bitrates (50 Mbps, 100 Mbps, 200 Mbps) and multiple configurations (DCF, DL OFDMA, DL/UL OFDMA, DL/UL OFDMA with MU-MIMO). Costas plays <strong>Half-Life: Alyx</strong>, while Miguel and Daniele play <strong>SteamVR Home</strong>. Each configuration includes single-user (SU) results as well, that can be used as a baseline for the multi-user (MU) ones.</p>
Related with: Long-short term memory prediction of user's locomotion in Virtual Reality publication (Dataset)
<p>Dataset: Captured motion data from 44 users.</p> <p>Scenes:</p> <p>SL -> Scene Lab.</p> <p>SR -> Escape Room.</p> <p>MF -> Shooter forest.</p> <p>Since it is recorded inside a game engine and all records take place inside their processing, the timestamp is written down for each register (Time_sice_startup field). Additionally, the anonymized identification of the user is recorded (User field).</p> <p>The dataset includes the following characteristics for Oculus Quest 2 HMD and each controller.</p> <ul> <li> DevicePosition (x, y, z): Position recorded.</li> <li> DeviceRotation (w, x, y, z): Rotation expressed with a quaternion.</li> <li> Forward (x, y, z): The unit vector that points to the specific device in the forward direction used in our new model. It can also be obtained by rotating $(0,0,1)$ with the quaternion.</li> <li> DeviceVelocity (x, y, z): Linear velocity of that device in that frame. It represents the rate of change in position.</li> <li> DeviceAcceleration (x, y, z): Linear acceleration of that device in that frame.</li> <li> DeviceAngularVelocity (x, y, z): The angular velocity vector in that frame of the device is measured in radians per second.</li> <li> DeviceAngularAcceleration (x, y, z): The angular acceleration at that frame. </li> </ul> <p>Also for each goal in the scene:</p> <ul> <li> GoalName (x, y, z): Position of that goal. If the element is static, the same position will always be recorded.</li> <li> GoalName_Quat (w, x, y, z): As in the previously defined fields, a rotation is expressed as a quaternion.</li> <li> GoalName_LocalScale (x, y, z): Scale of that element locally related to its parent in the hierarchy. They have no relatives in their hierarchy, so it is the real scale.</li> </ul>
Extending the application of connectivity metrics within the framework of the characterization of the dynamic behaviour of a WDS subjected to users' activity
<p>Water distribution networks (WDNs) are complex combinations of nodes and links, and the current tendency is to modify their topological structure through the closure of isolation valves for monitoring and water quality reasons. For their analysis, several approaches based on graph theory have recently been proposed, mainly considering steady-state flow conditions. However, in their real functioning, WDNs are continuously subjected to pressure transients generated by manoeuvres on regulation devices or by users’ activity. This study investigates the application of some metrics from graph theory, already used in the context of steady-state analysis, for assessing the effects of changes in the topological structure of a network ‒ due for example to sectorization or branching operations ‒ on its transient response when subjected to manoeuvres on devices such as hydrants, pumps, etc. or users’ activity. The analysis shows that some connectivity metrics can effectively reflect the dynamic pressure behaviour of the network and, thus, provide useful indications for design and management operations taking into account unsteady flow features.</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.