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1,617 results for “users”
Viva con Agua de St. Pauli e.V. User Stories created by socio-trechnical walkthroughs
<p>In March 2016 six socio-technical walktroughs (STWT, Hermann et al. 2004) have been conducted to identify requirements for the technical tool Pool used by Viva con Agua de St. Pauli e.V. (VCA) as a socio-technical organization (Kunau 2006). Volunteers of the NGO, as well as employees and partners were invited to participate one weekend for one STWT workshop. Every workshop has covered two days (a weekend) and focused one specific business process of VCA. The participants were independently selected for each working procedure that became a topic for a workshop.</p> <p>During the workshops, the existing business processes have been identified in a first step and during a second one improved. Afterwards, the moderator of the workshops has derived user stories for new technical support functions needed to establish the reworked business processes. These user stories are collected in the user stories catalog that is published in this data publication.</p> <table align="left"> <tbody> <tr> <td>(Herrmann et al. 2004)</td> <td>Herrmann, T., Kunau, G., Loser, K.-U., and Menold, N. Socio-technical walkthrough: Designing technology along work processes. <em>Proceedings of the eighth conference on Participatory design Artful integration: interweaving media, materials and practices - PDC 04</em>, ACM Press (2004), 132.</td> </tr> <tr> <td>(Kunau 2006)</td> <td>Kunau, G. Facilitating computer supported cooperative work with socio-technical self-descriptions. Technische Universität Dortmund, (2006).</td> </tr> </tbody> </table> <p> </p>
Sparse camera volumetric video applications. A comparison of visual fidelity, user experience , and adaptability. Supplementary Video
<p>This video is a supplementary video material to the paper "Sparse camera volumetric video applications. A comparison of visual fidelity, user experience , and adaptability". It shows a comparision of five volumetric videos scenes, captured with three different sparse volumetric video applications. This video aims to visualize the difference in fidelity and artifacts that each system expresses.</p>
User Stories made by Users Workshop Analysis Results
<p>In order to enable members of a socio-technical evolutionary-teal organization to design their technical component, we conducted a workshop that structures the collaboration between technical trained participants and non-trained participants. The workshop aims to transform "vague needs" into technical descriptions in the form of user stories.</p> <p>The workshop is the second part of series of workshops all limited to two hours. It uses the methods of <em>Design Thinking</em> and <em>Participatory Design</em>.</p> <p>The workshop has been recorded in video and the resulting data set has been published on Zenodo:</p> <p>Sell, Johann, & John, Elias. (2020). User Stories made by Users Workshop Data Set (1.0.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3898358</p> <p>A qualitative analyzes has been conducted covering four iterations of coding. This data set shows the results of last iteration and the resulting insights are referenced by a research paper that is currently under review.</p> <p>We hope that the material can be used to (a) comprehend the interpretation used in our qualitative research, and to (b) investigate other interesting research questions.</p>
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects - Raw Data
<p><strong>Explanation/Overview:</strong></p> <p>Corresponding raw data for the analyses 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 raw data is in the <code>.json</code> format and can be read by most languages/tools. It is recommended to import the data into a MongoDB to work with it.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis for possible further examinations, involving additional (not yet analysed) features such as the content of the comments etc. and also new ways of extracting networks.</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 three files:</p> <ul> <li><code>Comments.json</code> <ul> <li>contains the basic data representation with multiple fields (e.g., <code>time_created</code>, <code>user_login</code>). Each data field represents a comment.</li> </ul> </li> <li><code>Discussions.json</code> <ul> <li><code></code>contains all discussions. Each data field is a discussion, with multiple fields (e.g., <code>comments_count</code>, <code>user_login</code>)</li> </ul> </li> <li><code>Projects.json</code> <ul> <li><code></code>contains all projects. Each data field is a project, with multiple fields (e.g., <code>project_id</code>, <code>description</code>)</li> </ul> </li> </ul> <p><strong>Grouping:</strong></p> <p>The projects (and thus the corresponding discussions and comments) were collected on the basis of common forum features such as the discussion boards.</p>
Performance of users with Cerebral Palsy playing GABLE Games together with their results to the Left/Right Dynamic balance tool
<p>This dataset contains data generated by users of GABLE platform. The data shows the performance of some users with Cerebral Palsy playing GABLE Games together with their results to the Left/Right Dynamic balance tool. More information about GABLE project can be found at: www.projectgable.eu</p>
User-centered Usability Analysis of 41 Open Government Data Portals
<p>The data were collected during the user-centered analysis of usability of 41 open government data portals including EU27, applying a common methodology to them, considering aspects such as specification of open data set, feedback and requests, further broken down into 14 sub-criteria. Each aspect was assessed using a three-level Likert scale (fulfilled - 3, partially fulfilled - 2, and unfulfilled – 1), that belongs to the acceptability tasks. This dataset summarises a total of 1640 protocols obtained during the analysis of the selected portals carried out by 40 participants, who were selected on a voluntary basis. This is complemented with 4 summaries of these protocols, which include calculated average scores by category, aspect and country. These data allow comparative analysis of the national open data portals, help to find the key challenges that can negatively impact users’ experience, and identifies portals that can be considered as an example for the less successful open data portals.</p>
MoTiV: a Dataset of European User Mobility for Behavioral-Data
<p>Mobility is a system involving several stakeholders. Therefore, it is relevant to characterize mobility behavior and preferences in a detailed way, to enable nuanced decisions. Current paradigms rely mostly on time saving, proposing to users solutions that include the shortest path. Even though the value of travel time can be extended beyond travel duration, no dataset to characterize mobility and value of travel time from different perspectives exists. This creates a gap between novel mobility paradigms and the characterization of user mobility. To enable the mining of user mobility under these new paradigms, in this paper, we present the MoTiV (Mobility and Time Value) dataset, which contains data about travelers and their journeys, collected from a mobile application, called Woorti. Each trip contains multi-faceted information: from the transport mode, through its evaluation, to the positive/negative experience factors. We also present a use case, which compares corresponding legs with different transport modes, studying experience factors that negatively impact users. We conclude by discussing other application domains and research opportunities enabled by the dataset.</p>
Dataset for: A systematic literature review on user factors to support the sense of presence
<p>This dataset was created for a publication of Wiepke, Axel and Heinemann, Birte called "A systematic literature review on user factors to support the sense of presence". In this paper we used the PRISMA-method to collect Papers via Google Scholar on the third of April 2023 with the search term:<br>(framework OR model OR frameworks OR models OR processes OR ontologies) AND ((“personality traits” OR “personality variables” OR “personality factors”) AND “spatial presence”) AND (“virtual reality”) AND (learn OR edu\*)</p> <p>The results were pictured in "agreed" findings, where more than 50% of found studies supported a category of results and in "controversial", where there were significant findings, but less than 50% of the studies reported significance.</p> <p>This dataset contains:</p> <ul> <li>raw data for our literature review in .bib</li> <li>our main findings with categories in .csv</li> <li>a short Jupyter notebook script for one graphic in ipynb</li> <li>other graphics as .png</li> </ul>
Results of Survey on Playertypes by Gamification User Types Hexad Framework in Higher Education
<p>Survey on playertypes via the validated quesitonaire published in Krath, J., von Korflesch, H.F.O. (2021). Player Types and Game Element Preferences: Investigating the Relationship with the Gamification User Types HEXAD Scale. In: Fang, X. (eds) HCI in Games: Experience Design and Game Mechanics. HCII 2021. Lecture Notes in Computer Science(), vol 12789. Springer, Cham. https://doi.org/10.1007/978-3-030-77277-2_18</p> <p>Between 25.01.23 and 08.02.23 students of the University of Lübeck, Germany were invited to fill out an online questionnaire. The acquisition was done by sending an email to the students. No incentive was offered for participation, except to find out at one's own expression at the end of the survey. In addition to the validated questions, this also included questions about gender and study area. All participants agreed to anonymous data collection and publication. Participants were also asked to confirm that they were completing the survey for the first time, otherwise the return was removed from the result set. </p> <p>The result set is formatted as CSV. Questions and identifiers of the data are shown in the first line.</p>
SAAM Sleep Coaching User Feedback
<p>The data set was collected within the SAAM Project (Supporting Active Aging through Multimodal coaching, https://saam2020.eu/), and was used to develop a preference learning methodology with the aim of improving user's acceptance of coaching actions. The data set contains a set of sleep quality coaching actions and userćs feedback for each of them.</p>
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>
SenTopX: A Benchmark Twitter Dataset for User Sentiment on Various Topics
<p>This is a longitudinal Twitter dataset of 143K users during the period 2017-2021. The following is the detail of all the files:</p> <ul> <li><a href="11243662" target="_blank" rel="noopener noreferrer">SenTopX_userIDs.txt</a>: contains user IDs of 143K Twitter users.</li> <li><a href="../api/records/11243662/draft/files/userIDs_tweetIDs.zip/content" target="_blank" rel="noopener noreferrer">userIDs_tweetIDs.zip</a>: contains Tweet IDs of users, the name of the file is the user ID and the file contains the list of all the tweet IDs.</li> <li><a href="../api/records/11243662/draft/files/users_16_perspective_toxicity_scores.csv/content" target="_blank" rel="noopener noreferrer">users_16_perspective_toxicity_scores.csv</a> contains user IDs and 16 median Perspective API scores, the vector is shared as mean, median, and Gini Index of scores calculated over all tweets of a user.</li> <li><a href="../api/records/11243662/draft/files/LDAvis_top30_words_for_extracted_topics.csv/content" target="_blank" rel="noopener noreferrer">LDAvis_top30_words_for_extracted_topics.csv</a> contains the top 30 most relevant words extracted from each topic extracted by tweet-level topic modeling using the BERTweet topic model.</li> <li><a href="../api/records/11243662/draft/files/topic_modelling_statistics_per_user.csv/content" target="_blank" rel="noopener noreferrer">topic_modelling_statistics_per_user.csv</a> contains important and relevant statistics related to topic modeling results: <ul> <li> <p>1. user: This column represents the identifier for the user. Each row in the CSV corresponds to a specific user, and this column helps to track and differentiate between the users.</p> <p>2. avg_topic_probability: This column contains the average probability of the topics for each user calculated across all of the tweets in order to compare users in a meaningful way. It represents the average likelihood that a particular user discusses various topics over the observed period.</p> <p>3. maximum_topic_avg: This column holds the value of the highest average probability among all topics for each user. It indicates the topic that the user most frequently discusses, on average.</p> <p>4. index_max_avg_topic_probability_200: This column specifies the index or identifier of the topic with the highest average probability out of 200 possible topics. It shows which topic (out of 200) the user discusses the most.</p> <p>5. global_avg: This column includes the global average probability of topics across all users. It provides a baseline or overall average topic probability that can be used for comparative purposes.</p> <p>6. max_global_avg: This column contains the maximum global average probability across all topics for all users. It identifies the most discussed topic across the entire user base.</p> <p>7. index_max_global_avg: This column shows the index or identifier of the topic with the highest global average probability. It indicates which topic (out of 200) is the most popular across all users.</p> <p>8. entropy_200_topic: This column represents the entropy of the topics for each user, calculated over 200 topics. Entropy measures the diversity or unpredictability in the user's discussion of topics, with higher entropy indicating more varied topic discussion.</p> <p>In summary, these columns are used to analyze the topic engagement and preferences of users on a platform, highlighting the most frequently discussed topics, the variability in topic discussions, and how individual user behavior compares to overall trends.</p> </li> </ul> </li> </ul>
Anthropomorphic Mechanisms for User Acceptance in Human-Robot Interaction - PRISMA pass data
<p>This is the data produced in the course of selecting relevant literature for the <em>"User Acceptance in Human-Robot Interaction"</em> literature review article.</p> <p><strong>Contents:</strong></p> <ul> <li>Initial pass records: <em>prisma0_wos.xlsx + prisma0_scopus.xlsx</em></li> <li>Initial pass eligibility assessment:<em><strong> </strong>prisma0_eval.xlsx</em></li> <li>Second pass records, filtering and coarse assessment:<em><strong> </strong>prisma1.xlsx</em></li> <li>Third pass records, filtering and coarse assessment:<em><strong> </strong>prisma2.xlsx</em></li> <li>Fine eligibility assessment of 2nd and 3rd pass: <em>prisma_avalanche_1_and_2_report_update_04_26.pdf</em></li> </ul> <p> </p>
Software developers are users of the Heureka microservice platform
<p>This data set contains the qualitative analysis of the "software developers are users" study, conducted to investigate the fitting of the Heureka microservice platform (http://doc.soteto.net) to support software developers in participating the change of socio-technical evolutionary-teal organizations.</p> <p>The study is part of the SOTETO project (http://soteto.net) and its results will be published and contextualized by the dissertation thesis of Johann Sell.</p>
PAsCAL WP6 Pilot 3 Autonomous Bus Line Datasets (Passengers and Co-Road Users)
<p>These two datasets were collected within the context of the PAsCAL research project between September 2021 and March 2022 on the campus of the UAM University in Madrid, Spain. Subject of the pilot was a Level 4 autonomous bus shuttle, which is to date one of the only shuttles in Europe to run in open traffic. Due to this and the fact that only a steward is on-board of the vehicle in case of incidences or passenger support, two surveys were designed:</p> <ol> <li>Survey for Shuttle Users: Passengers experienced the ride on the autonomous shuttle within the context of the multi-modal trip, connecting them to an interurban train station and an interurban (long-distance) bus station on the other side. Purpose of the survey was to capture the participant's overall acceptance and attitude towards the vehicle after using it and comparing it directly to available traditional modes of transport.</li> <li>Survey for Shuttle Co-Road Users: Since the shuttle is operating in open traffic, co-road users were also stopped randomly and asked to complete the survey to map the acceptance of the autonomous shared and public vehicle they were sharing the road with. This included not just car drivers, but also pedestrians and cyclists on-site.</li> </ol> <p>In order to analyse the answers given to the questions, it is recommended to consult also the "PAsCAL WP6 Pilots Surveys" dataset, which contains all questions and possible answers.</p>
[Dataset] Does Volunteer Engagement Pay Off? An Analysis of User Participation in Online Citizen Science Projects
<p><strong>Explanation/Overview:</strong></p> <p>Corresponding dataset for the analyses and results achieved in the CS Track project in the research line on participation analyses, which is also reported in 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>. The usernames have been anonymised.</p> <p><strong>Purpose:</strong></p> <p>The purpose of this dataset is to provide the basis to reproduce the results reported in the associated deliverable, and in the above-mentioned publication. As such, it <strong>does not</strong> represent <strong>raw data</strong>, but rather files that already include certain analysis steps (like calculated degrees or other SNA-related measures), ready for analysis, visualisation and interpretation with R.</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>In this Zenodo entry, several files can be found. The structure is as follows (<code>files</code> and <strong>folders </strong>and<strong> </strong><em>descriptions</em>).</p> <ul> <li><code>corresponding_calculations.html</code> <ul> <li><em>Quarto-notebook to view in browser</em></li> </ul> </li> <li><code>corresponding_calculations.qmd</code> <ul> <li><em>Quarto-notebook to view in RStudio</em></li> </ul> </li> <li><strong>assets</strong> <ul> <li><strong>data</strong> <ul> <li><strong>annotations</strong> <ul> <li><code>annotations.csv</code> <ul> <li><em>List of annotations made per day for each of the analysed projects</em></li> </ul> </li> </ul> </li> <li><strong>comments</strong> <ul> <li><code>comments.csv </code> <ul> <li><em>Total list of comments with several data fields (i.e., comment id, text, reply_user_id)</em></li> </ul> </li> </ul> </li> <li><strong>rolechanges</strong> <ul> <li><code>478_rolechanges.csv</code> <ul> <li><em>List of roles per user to determine number of role changes </em></li> </ul> </li> <li><code>1104_rolechanges.csv</code> <ul> <li><em>...</em></li> </ul> </li> <li><code>...</code></li> </ul> </li> <li><strong>totalnetworkdata</strong> <ul> <li><strong>Edges</strong> <ul> <li><code>478_edges.csv</code> <ul> <li><em>Network data (edge set) for the given projects (without time slices)</em></li> </ul> </li> <li><code>1104_edges.csv</code> <ul> <li><em>...</em></li> </ul> </li> <li><code>...</code></li> </ul> </li> <li><strong>Nodes</strong> <ul> <li><code>478_nodes.csv</code> <ul> <li><em>Network data (node set) for the given projects (without time slices)</em></li> </ul> </li> <li><code>1104_nodes.csv</code> <ul> <li><em>...</em></li> </ul> </li> <li><code>...</code></li> </ul> </li> </ul> </li> <li><strong>trajectories</strong> <ul> <li><em>Network data (edge and node sets) for the given projects and all time slices (Q1 2016 - Q4 2021)</em></li> <li><strong>478</strong> <ul> <li><strong>Edges</strong> <ul> <li> <p><code>edges_4782016_q1.csv</code></p> </li> <li> <p><code>edges_4782016_q2.csv</code></p> </li> <li> <p><code>edges_4782016_q3.csv</code></p> </li> <li> <p><code>edges_4782016_q4.csv</code></p> </li> <li> <p><code>...</code></p> </li> </ul> </li> <li><strong>Nodes</strong> <ul> <li><code>nodes_4782016_q1.csv</code></li> <li> <p><code>nodes_4782016_q4.csv</code></p> </li> <li> <p><code>nodes_4782016_q3.csv</code></p> </li> <li> <p><code>nodes_4782016_q2.csv</code></p> </li> <li> <p><code>...</code></p> </li> </ul> </li> </ul> </li> <li> <p><strong>1104</strong> </p> <ul> <li> <p><strong>Edges</strong> </p> <ul> <li> <p><code>...</code></p> </li> </ul> </li> <li> <p><strong>Nodes</strong> </p> <ul> <li> <p><code>...</code></p> </li> </ul> </li> </ul> </li> <li> <p>...</p> </li> </ul> </li> </ul> </li> <li><strong>scripts</strong> <ul> <li><code>datavizfuncs.R</code> <ul> <li><em>script for the data visualisation functions, automatically executed from within </em><code>corresponding_calculations.qmd</code></li> </ul> </li> <li><code>import.R</code> <ul> <li><em>script for the import of data, automatically executed from within </em><code>corresponding_calculations.qmd</code></li> </ul> </li> </ul> </li> </ul> </li> <li><strong>corresponding_calculations_files</strong> <ul> <li>f<em>iles for the html/qmd view in the browser/RStudio</em></li> </ul> </li> </ul> <p><strong>Grouping:</strong></p> <p>The data is grouped according to given criteria (e.g., <code>project_title </code>or <code>time</code>). Accordingly, the respective files can be found in the data structure</p>
MEDIATOR Driving Simulator Study Germany: Questionnaire Data User Evaluation HMI
<p>The dataset provided resulted from a driving simulator study conducted by Chemnitz University of Technology (TUC) within work package 3 of the MEDIATOR project. The study focused on the user evaluation of the Mediator system and its functionalities, including the innovative Human Machine interface (HMI). The user evaluation centred on acceptance, trust, usability, comfort and the experience of Transitions of Control (TOCs). The core idea of the Mediator system is to mediate between the human driver and the automated system. Thereby, the Mediator system aims at establishing both as a team that is aware of each other’s strengths, limitations as well as current states in order to achieve safe TOCs, which are actively proposed by the HMI. The main focus of the driving simulator study was on comfort TOCs from manual to automated driving, simulated automation degradation and related TOCs by the human driver, comfort critical situations (i.e., close approach to the rear-end of a traffic jam) as well as the influence of driver characteristics. The provided dataset contains the questionnaire data of 74 German-speaking participants.</p> <p> </p> <p>This document contains information about the study methodology and coding of the variables. For a detailed description, please consult Mediator deliverable D3.3 ‘Results of the MEDIATOR driving simulator evaluation studies’ (Part II – Driving simulator study Germany). Please note that selected passages of this deliverable were adopted (partly in a slightly modified manner) in this document.</p> <p> </p> <p>This dataset is licensed under a <a href="https://spdx.org/licenses/CC-BY-4.0.html">Creative Commons Attribution 4.0 International</a> License.</p> <p> </p> <p>The research leading to this dataset received funding from the European Commission Horizon 2020 programme under the project MEDIATOR (<a href="https://mediatorproject.eu/">https://mediatorproject.eu/</a>), grant agreement number 814735.</p> <p> </p> <p>If you use the dataset, please cite it as: MEDIATOR (2023). MEDIATOR Driving Simulator Study Germany: Questionnaire Data User Evaluation HMI. <a href="https://doi.org/10.5281/zenodo.7638299">https://doi.org/10.5281/zenodo.7638299</a></p> <p> </p> <p>For further information, please contact: <a href="mailto:cornelia.hollander@psychologie.tu-chemnitz.de">cornelia.hollander@psychologie.tu-chemnitz.de</a>.</p>
Multi-Dimensional Data Viewer (MDV) user manual for data exploration: "Systematic analysis of YFP traps reveals common discordance between mRNA and protein across the nervous system"
<table> <tbody> <tr> <td> <p> Please also see the latest version of the repository:<br> <a href="https://doi.org/10.5281/zenodo.6374011">https://doi.org/10.5281/zenodo.6374011</a> and<br> our website: <a href="https://ilandavis.com/jcb2023-yfp">https://ilandavis.com/jcb2023-yfp</a></p> </td> </tr> </tbody> </table> <p> </p> <p>The explosion in the volume of biological imaging data challenges the available technologies for data interrogation and its intersection with related published bioinformatics data sets. Moreover, intersection of highly rich and complex datasets from different sources provided as flat csv files requires advanced informatics skills, which is time consuming and not accessible to all. Here, we provide a “user manual” to our new paradigm for systematically filtering and analysing a dataset with more than 1300 microscopy data figures using Multi-Dimensional Viewer (MDV) -<a href="https://mdv.molbiol.ox.ac.uk/projects/mdv_project/7012?view=RNA+%2F+Protein+Distribution">link</a>, a solution for interactive multimodal data visualisation and exploration. The primary data we use are derived from our published systematic analysis of 200 YFP traps reveals common discordance between mRNA and protein across the nervous system (<a href="https://doi.org/10.1083/jcb.202205129">eprint link</a>). This manual provides the raw image data together with the expert annotations of the mRNA and protein distribution as well as associated bioinformatics data. We provide an explanation, with specific examples, of how to use MDV to make the multiple data types interoperable and explore them together. We also provide the open-source python code <a href="https://github.com/ilandavislab/Annotate.OMERO.Fig">(github link)</a> used to annotate the figures, which could be adapted to any other kind of data annotation task.</p>
Supplementary Materials for "Exploration of User Privacy in 802.11 Probe Requests with MAC Address Randomization Using Temporal Pattern Analysis"
<p>Supplementary Materials for "Exploration of User Privacy in 802.11 Probe Requests with MAC Address Randomization Using Temporal Pattern Analysis"</p> <p>This package contains an anonymized packets of 802.11 probe requests captured in in December 2021 at Universitat Jaume I . The packet capture file is in the standardized *.pcap binary format and can be opened with any packet analysis tool such as Wireshark or scapy (Python packet analysis and manipulation package).</p>
Data set of two dual-task paradigms to measure listening effort in cochlear implant users
<p>This data set presents the data from the paper by Hendrikse, Dingemanse, & Goedegebure (2022). This study aimed to investigate the feasibility of using listening effort to measure relatively small differences in SNR, as would arise from different hearing-device settings. Listening effort was chosen, because there are indications in literature that listening effort may be more sensitive to differences between hearing-device settings than established speech intelligibility measures. Two behavioral listening effort tests were performed at two signal-to-noise ratios (SNRs) where the intelligibility was high. A sentence final word identification and recall test (SWIRT), and a sentence verification test (SVT) were compared with a group of 18 Dutch CI users. SWIRT measured the ability to recall the final words of sentences after a list of five or seven sentences was presented. The SVT measured the ability and reaction time to determine whether a sentence was true or false. Both tests were conducted in background noise at SNRs +4 dB and +8 dB above the 50% speech perception threshold. The structure of the data files is explained in the README file.</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.