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29 results for “user support”
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>
Competence Centres and User Support Centres Landscaping Results
<p>The dataset contains the data collected from the landscaping activity related to the Competence Centre and user support network for the D7.1 Report on Competence Centres landscape and user support activities.</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>
Online Repository of the Study "I want to RIDE my e-bicycle!": Supporting Developers Categorizing User Issues of a Mobility-as-a-Service Platform
<p><strong>Online Repository of the Study </strong><em>“I want to RIDE my e-bicycle!": Supporting Developers Categorizing User Issues of a Mobility-as-a-Service Platform</em></p> <p><strong>Introduction</strong></p> <p>In the Mobility-as-a-Service (MaaS) context, e-bikes are important and environmental-friendly transportation resources providing flexibility, time and cost savings, and reducing traffic congestion. Additional to user satisfaction and marketing advantages, the resolution of user-reported issues is regulated in many cities. In order to efficiently solve the issues, it is essential to quickly identify their types (e.g., software- or hardware-related?) to assign them to the responsible team. But for popular e-mobility services, the manual analysis of the reports is inefficient because of its tediousness, high time requirements, and error-proneness. </p> <p>Our empirical study, carried out in the context of a <em>Mobility as a Service </em>start-up company, proposes an approach for the automated identification of relevant concerns reported by users of e-bike services. The company has more than 20,000 private customers across seven different countries and dedicates considerable effort in analyzing user behavior. However, the current manual process of analyzing and triaging user-reported issues hinders MaaS-company’s ability to grow and expand its services. </p> <p>To help MaaS providers identify relevant user-reported issues, In the study, we (i) manually inspect about 3,000 user-reported issues received by the MaaS company; (ii) design a taxonomy modeling the types of relevant issues reported by users; and (iii) propose MaaS-RIDE, an approach to automatically classify the user-reported issues according to the categories of the devised taxonomy. </p> <p>Our results demonstrate that MaaS-RIDE is able to accurately (F-measure ≥ 93%) identify software and hardware user-reported issues. This result is critical for e-bike sharing companies to address such issues in an agile way and achieve the required user satisfaction.</p> <p><strong>Dataset Overview</strong></p> <p>The dataset is composed of the following different sorts of data: </p> <ul> <li> “<em>Data_and_preprocessing</em>” folder <ul> <li>o the user-reported issues data</li> <li>o the user-reported issues data processed as Bag of Words for Machine Learning training. <ul> <li>For this look at the sub-folder “<em>input_data_for_ML</em>” and the following matrices: <ul> <li><em>tf-idf-matrix-of-comment_finals_with_oracle_info_low_level.csv</em></li> <li><em>tf-idf-matrix-of-comment_finals_with_oracle_info.csv</em></li> </ul> </li> <li>Moreover, a sample of selected issues was reported in the replication package: <ul> <li>see file “<em>randomSamples.csv</em>” (due to a non-disclosure agreement with our industrial partner, we are unauthorized to share the whole raw user reports used in our experiments)</li> <li> “RQ1” folder: Types of E-bikes User-reported Issues</li> </ul> </li> </ul> </li> <li> the resulting taxonomy after the analysis of the issues</li> <li> “RQ2” folder: Classifying E-bikes Issue types</li> <li> the trained models </li> <li> the results of the models</li> </ul> </li> </ul> <p>The following sections describe more in detail what each of those folders and files contain.</p> <p><strong>“Data_and_preprocessing” folder</strong></p> <ul> <li><strong>User-reported issues subset.</strong></li> </ul> <p>In an industrial setting, due to privacy reasons, we disclose only an example subset of the user-reported issues, this information is in the file <em>randomSamples.csv</em>.</p> <p>The <em>randomSamples.csv </em>a subset that was generated randomly adding 20 examples using a stratified sampling from the High-level categories and 20 from the Low-level categories. This subset is not exhaustive but serves the purpose of showing the reviewers the kind of issues that this particular industrial set is confronted with. The file contains:</p> <ul> <li> <ul> <li> the Id of the user report; </li> <li> the column "comment_final"<strong> </strong>contains the issue text after the replacement of information that needed anonymization (e.g., vehicle-plates, personal names, addresses and timestamps); </li> <li> the column "High_level_category" contains the selected category from the 5 first level categories of the presented <em>Three-level taxonomy of e-bike user reported issues</em>; </li> <li>• the columns ‘Low_level_category" and "Fine_grained_topic" contain the assigned, if existing, respective category. </li> </ul> </li> <li><strong>Bag of Words Term by Document matrix.</strong></li> </ul> <p>An important input for training the ML models is the Bag of Words representation generated after processing the 2,989 manually-labeled user issues. The result of this process is a Term-by-Document matrix. We share this matrix in the files in the sub-folder <em>input_data_for_ML </em>where they are labeled for High- and Low-level categories. </p> <p>In the <em>tf-idf-matrix-of-comment_finals_with_oracle_info.csv</em> and <em>tf-idf-matrix-of-comment_finals_with_oracle_info_low_level.csv</em> files, the first column refers to the issue “Id”, the last column “oracle” is the labeled category, the rest of the columns represent the terms contained in the 2,989 user-reported issues and in each row the weight of the i−𝑡ℎ term contained in the j−𝑡ℎ user issue by using the tf-idf score.</p> <p><strong>“RQ1” folder</strong></p> <ul> <li><strong>“Three-level taxonomy of e-bike user-reported issues.pdf<em>” file</em></strong></li> </ul> <p>The taxonomy derives from the manual analysis of the 2,989 user issues. We found that a three-level taxonomy provides significant granularity to the MaaS-company. The taxonomy encompasses 5 High-level categories, 16 Low-level categories, and 15 Low-level subcategories of e-bike user-reported issues. The file <em>Three-level taxonomy of e-bike user-reported issues.pdf</em> presents the taxonomy categories and in the columns “Nr.” and “%” it shows the number of occurrences within the analyzed dataset, and the corresponding percentages.</p> <p><strong>“RQ2” folder</strong></p> <ul> <li><strong>“Trained Models” folder</strong></li> </ul> <p>We provide the trained machine and deep learning models in the sub-folder <em>ML_DL_models</em>. Our approach experimented with classic machine learning models based on the Bag-of-Words approach using SVM, on Word Embeddings using FastText, and Language models leveraging BERT. The SVM and BERT models were trained using the open source low-code data analytics platform KNIME and were used to classify issues corresponding to the first and second levels of the taxonomy from the “RQ1” folder. A 10-fold cross validation strategy was used to assess the classification performance. </p> <p>The fastText model was trained by using default values of parameters (https://fasttext.cc/docs/en/options.html) and a 10-fold cross-validation strategy. With fastText, we classified issues corresponding only to the first level of the taxonomy from “RQ1” folder, since fastText is more effective when more data points are available in the training set (i.e., lower levels in the taxonomy have fewer well-represented issue types).</p> <ul> <li><strong>“Model results” folder</strong></li> </ul> <p>In the sub-folder model_results we provide the tables summarizing the results of using the proposed MaaS-RIDE approach, with which we automatically identify and categorize user-reported issues according to the High-level and Low-level categories of the taxonomy devised in RQ1, which are relevant for the MaaS-company. </p>
Supporting data for: "Diaphysator: an online application for the exhaustive cartography and user-friendly statistical analysis of long bone diaphyses"
<p>Example of dataset to be used with the R-shiny application “Diaphysator”, composed of right tibiae and femora.</p> <p>These data file have been published in: Lacoste Jeanson, A., Santos, F., Villa, C., Banner, J., & Bruzek, J. (2018). Architecture of the femoral and tibial diaphyses in relation to body mass and composition: Research from whole-body CT. <em>American Journal of Physical Anthropology</em>, 167, 813– 826. doi: <a href="https://onlinelibrary.wiley.com/doi/abs/10.1002/ajpa.23713">10.1002/ajpa.23713</a></p> <p>This zip file contains:</p> <ul> <li>an “Information file” in CSV format</li> <li>various data files for human femora and tibiae in CSV format</li> </ul> <p>For all CSV files, the field separator is the comma “,” and the character used for decimal points is the dot “.”</p>
Supporting live data sessions in multi-user virtual reality using CAVA360VR
<p>A dense composite 2D video clip showing the different screens and audio chat of three analysts using <em>CAVA360VR </em>(Collaborate, Annotate, Visualise, Analysis 360 video in Virtual Reality) to hold a live virtual data session at a national research conference in 2019. The three co-analysts (M, J and P) in VR were collaborating even though they were in three different physical locations across two countries. The video data that they analysed together involved multicam recordings of six participants in a two-team competition to solve a Lego puzzle when only one member in each group could view the hidden Lego construction in order to instruct the others in their team how to rebuild it from a pile of blocks. The video clip shows the viewport chosen by each of the three analysts. There are three possible viewports that can be selected: a) what they each see in their VR HMD (head-mounted display), b) an over-the-shoulder view (with an outline showing their avatar), and c) a static third-person view of the three analysts. A fourth screen shows the view of an observer in non-VR mode.</p>
Supporting user preferences in search-based product line architecture design using Machine Learning
<p>The Product Line Architecture (PLA) is one of the most important artifacts of a Software Product Line. PLA design requires intensive human effort as it involves several conflicting factors. In order to support this task, an interactive search-based approach, automated by a tool named OPLA-Tool, was proposed in a previous work. Through this tool the software architect evaluates the generated solutions during the optimization process. Considering that evaluating PLA is a complex task and search-based algorithms demand a high number of generations, the evaluation of all solutions in all generations cause human fatigue. In this work, we incorporated in OPLA-Tool a Machine Learning (ML) model to represent the architect in some moments during the optimization process aiming to decrease the architect's effort. Through the execution of a quanti-qualitative exploratory study it was possible to demonstrate the reduction of the fatigue problem and that the solutions produced at the end of the process, in most cases, met the architect’s needs.</p>
Educational Program for Hearing Aid Users With Internet Support
ClinicalTrials.gov study NCT01837550. IPD Sharing: NO. Countries: 1. Publications: 1.
Supporting user preferences in Search-Based Product Line Architecture Design using Machine Learning
<p>Presentation of the paper Supporting user preferences in Search-Based Product Line Architecture Design using Machine Learning to the SBCARS 2020.</p>
Validation of the pelvic attachment mechanism of NIMBLE, an ambulatory user support gait rehabilitation robot.
<p>Description of the project<br><span><span>NIMBLE project aims to design and validate a novel gait rehabilitation robot. The objective of the robot is to provide ambulatory, body-weight supported walking training assisting user's limbs and its center of mass. The NIMBLE robot is comprised by a robotic frame, a lower limb exoskeleton, and a exoskeleton-frame coupling mechatronics that allows to assist the user's center of mass.</span></span></p> <p>Description of the dataset<br><span><span>This dataset contains the results of an experiment conducted to assess the alteration on walking kinematics due to the mechatronic device coupling the frame and the exoskeleton.</span></span></p> <p>--------------------------<br>METHODOLOGY<br>--------------------------<br>1. Methodology<br>The walking kinematics were recorded and analyzed at slow (0.5 m/s) and normal (1 m/s) speeds under three different conditions: unrestricted walking, walking while wearing the Exo-H3 corset, and walking while wearing the Exo-H3 corset coupled to the robotic frame. Only the corset is worn, to isolate and evaluate the direct impact of the designed mechanism without the kinematic restrictions imposed by the exoskeleton. This approach ensures that the effects observed are solely attributable to the mechanism itself, avoiding any confounding influences from the exoskeleton. This approach allows the evaluation of the influence of the exoskeleton corset, as it is a required component to secure the user to the robotic frame, and a comparison of its impact with that of the robotic frame. For this purpose, joint angles and their maximum and minimum values and ranges of motion (ROM) are analyzed in both the sagittal and frontal planes, along with the step width and the displacement of the CoM in the vertical and horizontal directions.</p> <p>2. Software<br>Data were collected using Kinovea and then processed and analyzed using Matlab.</p> <p>--------------------------<br>FILES<br>--------------------------<br>1. Files <br>One ".m" file is included. For each subject (idX), three subfolders are found:<br>- Calib folder: it contains the initial position and angular value of the markers and angles analyzed.<br>- frontal folder: for both speeds and the three analyzed conditions (Unrestricted walking CL, wearing the corset CC, and attached to the robotic frame CML), it contains the raw data, filtered data, segmented data normalized by gait cycles, mean and standard deviation data of those segmented cycles, and normalized data using the initial value from Calib file, for every marker position in the frontal plane (ankle, knee, hip and center of mass). Also, segmented, mean and deviations are presented from angular values in the frontal plane (right and left hip abduction and pelvic list).<br>- sagittal folder: for both speeds and the three analyzed conditions (Unrestricted walking CL, wearing the corset CC, and attached to the robotic frame CML), it contains the raw data, filtered data, segmented data normalized by gait cycles, mean and standard deviation data of those segmented cycles, and normalized data using the initial value from Calib file, for every marker position in the sagittal plane (hip, knee, ankle and toe tip). Also, segmented, mean and deviations are presented from angular values in the frontal plane (hip, knee and ankle flexo-extension).</p> <p>In addition, one average folder (media) includes average data for the statistical analysis. Signals subfolder includes all segmented data from the previous variables for every speed and conditions, joining the data from every subject in one table, a total of 50 samples (10 segmented cycles x 5 subjects). Value subfolder includes maximum, minimum and ranges of motion of angular variables. Every variable has 50 rows (10 segmented cycles x 5 subjects) and 3 columns (CL, CC, and CML conditions). Also mean step width is presented, as the distance between ankle markers in the frontal plane.</p> <p>--------------------------<br>OTHERS<br>--------------------------<br>1. Data dictionary<br>speed05: data collected at 0.5 m/s<br>speed1: data collected at 1 m/s<br>CL: Unrestricted walking condition<br>CC: Wearing the corset condition<br>CML: Attached to the robotic frame condition<br>R: right leg<br>L: left leg<br>x: horizontal direction<br>y: vertical direction<br>ANKLE, KNEE, HIP, COM: ankle, knee, hip, and virtual center of mass markers in the frontal plane.<br>HIP_lat, KNEE_lat, ANKLE_lat and TOE_lat: hip, knee, ankle, and toe tip markers in the sagittal plane.<br>Abd: hip abduction angle.<br>Hip_Angle, Knee_Angle, Ankle_Angle: hip, knee, and ankle flexo-extension angles.</p> <p>--------------------------<br>SPONSORSHIP INFORMATION AND GRANT IDs<br>--------------------------<br>1. Grant Information<br>This work is part of the R&D project PID2021-123657OB-C32, funded by MCIN/ AEI/10.13039/501100011033/ and by “ERDF A way of making Europe”.</p>
Supporting data for "SpheroScan: A User-Friendly Deep Learning Tool for Spheroid Image Analysis"
<p>In recent years, three-dimensional (3D) spheroid models have become increasingly popular in scientific research as they provide a more physiologically relevant microenvironment that mimics in vivo conditions. The use of 3D spheroid assays has proven to be advantageous as it offers a better understanding of the cellular behavior, drug efficacy, and toxicity as compared to traditional two-dimensional cell culture methods. However, the use of 3D spheroid assays is impeded by the absence of automated and user-friendly tools for spheroid image analysis, which adversely affects the reproducibility and throughput of these assays.</p> <p>To address these issues, we have developed a fully automated, web-based tool called SpheroScan, which uses the deep learning framework called Mask Regions with Convolutional Neural Networks (R-CNN) for image detection and segmentation. To develop a deep learning model that could be applied to spheroid images from a range of experimental conditions, we trained the model using spheroid images captured using IncuCyte Live-Cell Analysis System and a conventional microscope. Performance evaluation of the trained model using validation and test datasets shows promising results.</p> <p>SpheroScan allows for easy analysis of large numbers of images and provides interactive visualization features for a more in-depth understanding of the data. Our tool represents a significant advancement in the analysis of spheroid images and will facilitate the widespread adoption of 3D spheroid models in scientific research. The source code and a detailed tutorial for SpheroScan are available at <a href="https://github.com/FunctionalUrology/SpheroScan">https://github.com/FunctionalUrology/SpheroScan</a>.</p> Enable Ginger<em>Cannot connect to Ginger</em> Check your internet connection<br> or reload the browserDisable in this text fieldRephraseRephrase current sentenceEdit in Ginger×
Development and Implementation of an Online Psychological Support Program for Tobacco Users
ClinicalTrials.gov study NCT07001722. IPD Sharing: NO. Countries: 1. Publications: 10.
Activating Community Support for Substance Users
ClinicalTrials.gov study NCT03194295. IPD Sharing: NO. Countries: 1. Publications: 1.
Figure 4 from: Rose DC, Addison P, Ausden M, Bennun L, Mills C, O'Donnell SAL, Parker C, Ryan M, Weatherdon L, Despot-Belmonte K, Sutherland WJ, Robertson RJ (2017) Decision support tools in conservation: a workshop to improve user-centred design. Research Ideas and Outcomes 3: e21074. https://doi.org/10.3897/rio.3.e21074
Figure 4 - User testing of decision support tools. The list of tools exhibited were as follows: (1) Facilitator (Rowan Eisner), (2) Ecobat (Paul Lintott and Sophie Davison), (3) Local Evidence Assessment Tool (Claire Wordley, (4) IBAT (Kerstin Brauneder and Natasha Ali on behalf of IBAT Alliance), (5) Camgeocon (Dilkushi de Alwis Pitts), (6) Species+ (Kelly Malsch), (7) Protected Planet (Brian MacSharry), (8) TradeMapper (TRAFFIC), (9) LEFT (Peter Long), (10) NaturEtrade (Peter Long and Beccy Wilebore), and (11) WCS Offenders database (Andy Plumptre).
Figure 3 from: Rose DC, Addison P, Ausden M, Bennun L, Mills C, O'Donnell SAL, Parker C, Ryan M, Weatherdon L, Despot-Belmonte K, Sutherland WJ, Robertson RJ (2017) Decision support tools in conservation: a workshop to improve user-centred design. Research Ideas and Outcomes 3: e21074. https://doi.org/10.3897/rio.3.e21074
Figure 3 - Cost v benefit - using a tool takes effort so the benefits of use must clearly outweigh this (credit: Parker).
Figure 2 from: Rose DC, Addison P, Ausden M, Bennun L, Mills C, O'Donnell SAL, Parker C, Ryan M, Weatherdon L, Despot-Belmonte K, Sutherland WJ, Robertson RJ (2017) Decision support tools in conservation: a workshop to improve user-centred design. Research Ideas and Outcomes 3: e21074. https://doi.org/10.3897/rio.3.e21074
Figure 2 - Panel session on user engagement. From Left: Melanie Ryan (Luc Hoffmann Institute), Stephanie O' Donnell (WILDABS.NET, FFI), Dr Prue Addison (University of Oxford).
Figure 6 from: Rose DC, Addison P, Ausden M, Bennun L, Mills C, O'Donnell SAL, Parker C, Ryan M, Weatherdon L, Despot-Belmonte K, Sutherland WJ, Robertson RJ (2017) Decision support tools in conservation: a workshop to improve user-centred design. Research Ideas and Outcomes 3: e21074. https://doi.org/10.3897/rio.3.e21074
Figure 6 - Colour branding opportunities, very few in the purple to pink range. (credit: Vizzuality as presented by Craig Mills, CEO, Vizzuality).
Figure 5 from: Brake I, Duin D, Van de Velde I, Smith V, Rycroft S (2011) Who learns from whom? Supporting users and developers of a major biodiversity e-infrastructure. ZooKeys 150: 177-192. https://doi.org/10.3897/zookeys.150.2191
Figure 5 - Request processing time for issues. Time lapse between posting of issues and the first reply to this issue divided into request category and each category divided into issues posted by users versus developers (October 2010–June 2011).
Figure 3 from: Brake I, Duin D, Van de Velde I, Smith V, Rycroft S (2011) Who learns from whom? Supporting users and developers of a major biodiversity e-infrastructure. ZooKeys 150: 177-192. https://doi.org/10.3897/zookeys.150.2191
Figure 3 - Pattern of support requests and of new Scratchpads over time. Number of support requests posted to both support systems by users and number of new Scratchpads created per month (October 2010–June 2011).
Figure 2 from: Brake I, Duin D, Van de Velde I, Smith V, Rycroft S (2011) Who learns from whom? Supporting users and developers of a major biodiversity e-infrastructure. ZooKeys 150: 177-192. https://doi.org/10.3897/zookeys.150.2191
Figure 2 - Pattern of email requests over time. Number of email requests per month divided into request category and each category divided into emails sent by users versus developers (October 2010–June 2011).
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.