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19 results for “Environment design”
Improving students' privacy awareness – Analysis of a pilot survey to design a VR environment for self-paced learning
<p>In this research, we measured the knowledge of students at the University of Debrecen in the field of data privacy awareness, online and password security.</p> <p><strong>Description</strong></p> <ul> <li>In the questionnaire, green-highlighted answer signs the correct answer to each question.</li> <li>Total data set contains the answers to each question and the respondent's age.</li> <li>Correct/incorrect data set contains information if the answer is correct to each question, and it also contains the respondent's age. One means the answer was correct, and zero means the answer was incorrect.</li> </ul>
How to quantify factors degrading DNA in the environment and predict degradation for effective sampling design
<p>Extra-organismal DNA (eoDNA) from material left behind by organisms (non-invasive DNA: e.g., faeces, hair) or from environmental samples (eDNA: e.g., water, soil) is a valuable source of genetic information. However, the relatively low quality and quantity of eoDNA, which can be further degraded by environmental factors, results in reduced amplification and sequencing success. This is often compensated for through cost- and time-intensive replications of genotyping/sequencing procedures. Therefore, system- and site-specific quantifications of environmental degradation are needed to maximize sampling efficiency (e.g., fewer replicates, shorter sampling durations), and to improve species detection and abundance estimates. Using ten environmentally diverse bat roosts as a case study, we developed a robust modelling pipeline to quantify the environmental factors degrading eoDNA, predict eoDNA quality, and estimate sampling-site-specific ideal exposure duration. Maximum humidity was the strongest eoDNA-degrading factor, followed by exposure duration and then maximum temperature. We also found a positive effect when hottest days occurred later. The strength of this effect fell between the strength of the effects of exposure duration and maximum temperature. With those predictors and information on sampling period (before or after offspring were born), we reliably predicted mean eoDNA quality per sampling visit at new sites with a mean squared error of 0.0349. Site-specific simulations revealed that reducing exposure duration to 2-8 days could substantially improve eoDNA quality for future sampling. Our pipeline identified high humidity and temperature as strong drivers of eoDNA degradation even in the absence of rain and direct sunlight. Furthermore, we outline the pipeline's utility for other systems and study goals, such as estimating sample age, improving eDNA-based species detection, and increasing the accuracy of abundance estimates.</p>
Figures of Smart Contract Design via Modeling Environment: A proposal based on DasContract DSL
<p>Figures of "Smart Contract Design via Modeling Environment: A proposal based on DasContract DSL".</p>
Figure 4. The multi-agent overall environment for integrated management-Design and Development a Control and Monitoring System for Greenhouse Conditions Based-On Multi Agent System
<p> The value of the final product incorporates not only quantity issues but also<br> quality issues, which are difficult to be measured or even estimated. The environment of each agent<br> is defined by the same parameters that define the physical environment in addition to internal states<br> reported by each agent.</p>
Accompanying data for publication: "Learning the Optimal Power Flow: Environment Design Matters"
<p>All the data created for the publication "Learning the Optimal Power Flow: Environment Design Matters" by Wolgast and Nieße. The dataset contains all training runs performed, including the final neural network weights, meta-data about the training run, and various metrics during the course of training, which were used to generate the results and plots. The source code to re-produce the plots for the publication (and everything else) can be found on GitHub: https://github.com/Digitalized-Energy-Systems/rl-opf-env-design</p>
How to quantify factors degrading DNA in the environment and predict degradation for effective sampling design
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Replication Package: Block-based or Graph-Based? Why Not Both? Designing a Hybrid Programming Environment for End-users
<p><strong>Block-based or Graph-based? Why Not Both? Designing a Hybrid Programming Environment for End-users: Replication Package</strong></p> <p>This repository contains supplementary materials for the paper "Block-based or Graph-based? Why Not Both? Designing a Hybrid Programming Environment for End-users". We provide this data for transparency reasons and to support replications of our experiments.</p> <p><strong>Summary of files contained in this package</strong></p> <p>This package contains two parts:</p> <ul> <li> <p>The <code>data-analysis/</code> folder contains the raw dataset we collected for our experiment in CSV format, as well as scripts we used for our analyses.</p> <ul> <li>Column <code>ID</code> contains a unique 4-digit identifier for each participant that they were assigned throughout our study.</li> <li>Column <code>Group</code> contains the group (Blocks/Graph) that participants were randomly assigned to.</li> <li>Columns <code>Task1Time</code> and <code>Task2Time</code> contain the time participants spent to complete the two programming tasks of our study in minutes.</li> <li>Columns <code>Task1Success</code> and <code>Task2Success</code> contain a boolean value indicating whether the participants successfully completed the given task. Note that participants had unlimited attempts until they timed out after a strict time limit of 30 minutes, so if a participant was unsuccessful the corresponding time value is 30.</li> <li>Columns <code>Task1Tests</code> and <code>Task2Tests</code> contain the number of times a participant executed their code throughout a task, including their final submission if they were successful.</li> <li>Columns <code>LearnTask</code>, <code>ReadTask</code> and <code>WriteTask</code> contain the scores that participants gave to the task editor component of their assigned programming environment. There are 3 scores for the categories "learnability", "readability" and "writability". Scores are on a 5-point scale from 1 (worst) to 5 (best).</li> <li>Columns <code>LearnTrig</code>, <code>ReadTrig</code> and <code>WriteTrig</code> contain the scores that participants gave to the trigger editor component of their assigned programming environment. There are 3 scores for the categories "learnability", "readability" and "writability". Scores are on a 5-point scale from 1 (worst) to 5 (best).</li> <li>Columns <code>LearnComp</code>, <code>ReadComp</code> and <code>WriteComp</code> contain the scores that participants gave to their assigned assigned programming environment in direct comparison to the other alternative. There are 3 scores for the categories "learnability", "readability" and "writability". Unlike in the paper, where scores are on a scale from -2 to 2, the raw scores here are on a 5-point scale from 1 (strong preference for other environment) to 5 (strong preference for own environment).</li> <li>The script <code>successplot.py</code> was used to generate the success rate plot used in a figure in the paper</li> <li>The script <code>survival.py</code> was used to perform the survival analysis presented in the paper and generate the related figure.</li> <li>The script <code>batplot.py</code> was used to generate the 3x3 grid of ratings used in a figure in the paper.</li> </ul> </li> <li> <p>The <code>materials/</code> folder contains the tutorials and task descriptions we presented to study participants. It also contains the exact wording of pre-screening and post-experiemental survey questions.</p> <ul> <li>The image <code>pre-screening.png</code> shows the three pre-screening questions we used to determine whether our participants could be included in our study.</li> <li>The images <code>tutorial1_instructions.png</code> and <code>tutorial1_sim.png</code> contain the instructions and initial simulator state we provided to participants for the first programming tutorial. This tutorial did not provide starter code and was identical for both participant groups.</li> <li>The images <code>tutorial2_instructions.png</code> and <code>tutorial2_sim.png</code> contain the instructions and initial simulator state we provided to participants for the second programming tutorial. This tutorial was identical for both participant groups and provided participants with starter code, which is shown in the images: <ul> <li><code>tutorial2_code_main.png</code> for the main program in the left canvas</li> <li><code>tutorial2_code_move.png</code> for the definition of "Move box to the right".</li> </ul> </li> <li>The images <code>tutorial3_instructions_blocks.png</code>/<code>tutorial3_instructions_graph.png</code> and <code>tutorial3_sim.png</code> contain the instructions and initial simulator state we provided to participants for the third programming tutorial. This tutorial also provided participants with starter code, which is shown in the images: <ul> <li><code>tutorial3_code_main.png</code> for the main program in the left canvas</li> <li><code>tutorial3_code_pick.png</code> for the definition of "Pick up box"</li> <li><code>tutorial3_code_place.png</code> for the definition of "Place box"</li> </ul> </li> <li>The images <code>task1_instructions.png</code> and <code>task1_sim.png</code> contain the instructions and initial simulator state we provided to participants for the first programming task. The task did not provide starter code and the instructions were identical for both participant groups.</li> <li>The images <code>task2_instructions.png</code> and <code>task2_sim.png</code> contain the instructions and initial simulator state we provided to participants for the second programming task. The instructions were identical for both groups. This task also provided participants with starter code, which is shown in the images: <ul> <li><code>task2_code_main.png</code> for the main program in the left canvas</li> <li><code>task2_code_pick_prog.png</code> for the definition of "Pick up block"</li> <li><code>task2_code_load_trig_blocks.png</code>/<code>task2_code_load_trig_graph.png</code> for the definition of the trigger "Ready to load machine"</li> <li><code>task2_code_load_prog.png</code> for the definition of "Load and activate machine"</li> <li><code>task2_code_finished_trig_blocks.png</code>/<code>task2_code_finished_trig_graph.png</code> for the definition of the trigger "Machine finished"</li> <li><code>task2_code_finished_prog1.png</code> for the definition of "Get block from machine"</li> <li><code>task2_code_finished_prog2.png</code> for the definition of "Place block in bin"</li> </ul> </li> <li> <div>The document <code>post_survey_full.pdf</code> contains a raw export of the comprehension questions and post-experimental survey as they were presented to participants </div> </li> <li>The image <code>usability.png</code> shows the usability questions we used to determine a participant's rating of their assigned programming environment. The questions were identical for both participant groups.</li> <li>The images <code>comprehension_blocks_1.png</code> and <code>comprehension_blocks_2.png</code> show the program comprehension questions we used to determine whether participants in the Blocks group could understand more complex triggers.</li> <li>The images <code>comprehension_graph_1.png</code> and <code>comprehension_graph_2.png</code> show the program comprehension questions we used to determine whether participants in the Graph group could understand more complex triggers.</li> <li>The images <code>comparison_blocks.png</code> and <code>comparison_graph.png</code> show the images of triggers in the alternative environment that we showed to our participants before choosing their preferred environment. The questions were identical for both participant groups.</li> <li>The image <code>comparison.png</code> shows the questions we used to determine a participant's preference between the two programming environment alternatives.</li> </ul> </li> </ul>
data set for Design of architectural environment integration of cyber-physical systems based on image segmentation and retrieval technology
<p>This data set is used to implement the project Design of architectural environment integration of cyber-physical systems based on image segmentation and retrieval technology</p>
# Blocks? Graphs? Why Not Both? Designing and Evaluating a Hybrid Programming Environment for End-users: Replication Package
<p><strong>Blocks? Graphs? Why Not Both? Designing and Evaluating a Hybrid Programming Environment for End-users: Replication Package</strong></p> <p>This repository contains supplementary materials for the paper "Blocks? Graphs? Why Not Both? Designing and Evaluating a Hybrid Programming Environment for End-users". We provide this data for transparency reasons and to support replications of our experiemnts.</p> <p><em>Note: This package is anonymized for peer review purposes. We will provide contact information for the authors at a later date. We also plan to add interactive versions of our tasks and tutorials in an updated version to allow readers easier exploration/experimentation.</em></p> <p><strong>Summary of files contained in this package</strong></p> <p>This package contains two parts:</p> <ul> <li> <p>The <code>data-analysis/</code> folder contains the raw dataset we collected for our experiment in CSV format, as well as scripts we used for our analyses.</p> <ul> <li>Column <code>ID</code> contains a unique 4-digit identifier for each participant that they were assigned throughout our study.</li> <li>Column <code>Group</code> contains the group (Blocks/Graph) that participants were randomly assigned to.</li> <li>Columns <code>Task1Time</code> and <code>Task2Time</code> contain the time participants spent to complete the two programming tasks of our study in minutes.</li> <li>Columns <code>Task1Success</code> and <code>Task2Success</code> contain a boolean value indicating whether the participants successfully completed the given task. Note that participants had unlimited attempts until they timed out after a strict time limit of 30 minutes, so if a participant was unsuccessful the corresponding time value is 30.</li> <li>Columns <code>Task1Tests</code> and <code>Task2Tests</code> contain the number of times a participant executed their code throughout a task, including their final submission if they were successful.</li> <li>Columns <code>LearnTask</code>, <code>ReadTask</code> and <code>WriteTask</code> contain the scores that participants gave to the task editor component of their assigned programming environment. There are 3 scores for the categories "learnability", "readability" and "writability". Scores are on a 5-point scale from 1 (worst) to 5 (best).</li> <li>Columns <code>LearnTrig</code>, <code>ReadTrig</code> and <code>WriteTrig</code> contain the scores that participants gave to the trigger editor component of their assigned programming environment. There are 3 scores for the categories "learnability", "readability" and "writability". Scores are on a 5-point scale from 1 (worst) to 5 (best).</li> <li>Columns <code>LearnComp</code>, <code>ReadComp</code> and <code>WriteComp</code> contain the scores that participants gave to their assigned assigned programming environment in direct comparison to the other alternative. There are 3 scores for the categories "learnability", "readability" and "writability". Unlike in the paper, where scores are on a scale from -2 to 2, the raw scores here are on a 5-point scale from 1 (strong preference for other environment) to 5 (strong preference for own environment).</li> <li>The script <code>survival.py</code> was used to perform the survival analysis presented in the paper and generate the related figure.</li> <li>The script <code>batplot.py</code> was used to generate the 3x3 grid of ratings used in a figure in the paper.</li> </ul> </li> <li> <p>The <code>materials/</code> folder contains the tutorials and task descriptions we presented to study participants. It also contains the exact wording of pre-screening and post-experiemental survey questions.</p> <ul> <li>The image <code>pre-screening.png</code> shows the three pre-screening questions we used to determine whether our participants could be included in our study.</li> <li>The images <code>tutorial1_instructions.png</code> and <code>tutorial1_sim.png</code> contain the instructions and initial simulator state we provided to participants for the first programming tutorial. This tutorial did not provide starter code and was identical for both participant groups.</li> <li>The images <code>tutorial2_instructions.png</code> and <code>tutorial2_sim.png</code> contain the instructions and initial simulator state we provided to participants for the second programming tutorial. This tutorial was identical for both participant groups and provided participants with starter code, which is shown in the images: <ul> <li><code>tutorial2_code_main.png</code> for the main program in the left canvas</li> <li><code>tutorial2_code_move.png</code> for the definition of "Move box to the right".</li> </ul> </li> <li>The images <code>tutorial3_instructions_blocks.png</code>/<code>tutorial3_instructions_graph.png</code> and <code>tutorial3_sim.png</code> contain the instructions and initial simulator state we provided to participants for the third programming tutorial. This tutorial also provided participants with starter code, which is shown in the images: <ul> <li><code>tutorial3_code_main.png</code> for the main program in the left canvas</li> <li><code>tutorial3_code_pick.png</code> for the definition of "Pick up box"</li> <li><code>tutorial3_code_place.png</code> for the definition of "Place box"</li> </ul> </li> <li>The images <code>task1_instructions.png</code> and <code>task1_sim.png</code> contain the instructions and initial simulator state we provided to participants for the first programming task. The task did not provide starter code and the instructions were identical for both participant groups.</li> <li>The images <code>task2_instructions.png</code> and <code>task2_sim.png</code> contain the instructions and initial simulator state we provided to participants for the second programming task. The instructions were identical for both groups. This task also provided participants with starter code, which is shown in the images: <ul> <li><code>task2_code_main.png</code> for the main program in the left canvas</li> <li><code>task2_code_pick_prog.png</code> for the definition of "Pick up block"</li> <li><code>task2_code_load_trig_blocks.png</code>/<code>task2_code_load_trig_graph.png</code> for the definition of the trigger "Ready to load machine"</li> <li><code>task2_code_load_prog.png</code> for the definition of "Load and activate machine"</li> <li><code>task2_code_finished_trig_blocks.png</code>/<code>task2_code_finished_trig_graph.png</code> for the definition of the trigger "Machine finished"</li> <li><code>task2_code_finished_prog1.png</code> for the definition of "Get block from machine"</li> <li><code>task2_code_finished_prog2.png</code> for the definition of "Place block in bin"</li> </ul> </li> <li>The image <code>usability.png</code> shows the usability questions we used to determine a participant's rating of their assigned programming environment. The questions were identical for both participant groups.</li> <li>The images <code>comprehension_blocks_1.png</code> and <code>comprehension_blocks_2.png</code> show the program comprehension questions we used to determine whether participants in the Blocks group could understand more complex triggers.</li> <li>The images <code>comprehension_graph_1.png</code> and <code>comprehension_graph_2.png</code> show the program comprehension questions we used to determine whether participants in the Graph group could understand more complex triggers.</li> <li>The images <code>comparison_blocks.png</code> and <code>comparison_graph.png</code> show the images of triggers in the alternative environment that we showed to our participants before choosing their preferred environment. The questions were identical for both participant groups.</li> <li>The image <code>comparison.png</code> shows the questions we used to determine a participant's preference between the two programming environment alternatives.</li> </ul> </li> </ul>
Adding Beauty and Value to the Environment: North Vancouver Landscape Design
<p>Nestled along British Columbia's stunning shoreline, North Vancouver is a secure location for homeowners and wildlife enthusiasts. Living in North Vancouver is lovely because of its verdant surroundings, towering mountains, and serene bodies of water. To be completely appreciated, this natural beauty must nevertheless be meticulously organized and preserved. This article describes North Vancouver landscape design in great depth. That covers how improving gardens, adding landscaping, and maintaining property may improve the appearance of a house and raise its market worth.</p> <h1>North Vancouver Property Maintenance: Preserving the Natural Beauty</h1> <p>Owners of land in North Vancouver have both advantages and disadvantages from its distinctive topography and mild jungle climate. Though the region receives a lot of rain, which benefits the plants, property care must be maintained to prevent issues with erosion, overgrowth, and water.</p> <p>Knowing about the neighbourhood is essential to the seamless maintenance of your North Vancouver house. Everything about upkeep has to be planned to blend in with the surroundings, from routine grass care and pruning to erosion prevention and environmentally friendly irrigation techniques. Hiring professionals for North Vancouver property management guarantees that these tasks will be completed appropriately and thoughtfully. This will preserve the natural feel and shield the landscaping of your house.</p> <h2>Updating Outside Spaces with a Garden Renovation in North Vancouver</h2> <p>People often redo their gardens to make their outdoor living spaces feel fresh again when their tastes and the seasons shift. In <a href="https://goo.gl/maps/sYN36xktU7sxjX2p6" target="_blank" rel="noopener"><strong><span>North Vancouver Garden Renovation</span></strong></a>, where gardens link to the surrounding environment, there are many ways things may alter.</p> <p>A thorough evaluation of the present landscape, accounting for factors such as the kind of native plants present, the amount of sunlight they receive, and the makeup of the soil, is the first step in any successful garden rehabilitation project. Utilising the distinctive natural features of the region, landscape designers in North Vancouver can create gardens that blend in well with their environment.</p> <p>As there many landscapes in North Vancouver, there are as many methods to enhance a garden. Plan vibrant flower beds brimming with native species or serene meditation gardens inspired by the Zen-like tranquilly of the region. Through careful design, precise execution, and a dedication to sustainability, homeowners may revitalize their outdoor areas while maintaining the surrounding natural beauty.</p> <h2>In North Vancouver's Landscape Installations, Art and Nature Collide</h2> <p>Artsy gardening initiatives are a component of <a href="https://www.helpfulhomeservices.ca/" target="_blank" rel="noopener"><strong>North Vancouver landscape design</strong></a> to improve the appearance of residences. Just a few of the projects that can be done to create distinctive and quaint outdoor areas are a tumbling stream, a meandering stone walkway, or a comfortable outdoor fireplace.</p> <p>North Vancouver landscape alterations should be done with great consideration to maintaining the environment's purity. Long-lasting, locally produced materials like wood and natural stone not only look great with their surroundings but also lessen environmental impact during construction.</p> <p>One excellent approach to respect the history of the area and make people feel more a part of their surroundings is to use indigenous culture and historical themes into landscaping. Just two instances of how these well-considered additions transform North Vancouver landscaping from just cosmetic features to significant communal emblems are garden statues featuring traditional Coast Salish art and rain gardens that mimic the area's natural filtration systems.</p>
Design and preliminary set-up of an experimental apparatus for continuous measurement of soil – gas pressure fluctuations in active seismogenic environments
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Data from: Survival estimates strongly depend on capture-recapture designs in a disturbed environment inducing dispersal
Capture‐Recapture (CR) approaches are extensively used to estimate demographic parameters. Their robustness relies on the selection of suitable statistical models, but also on the sampling design and effort deployed in the field. In colonial or territorial species showing breeding site fidelity, recurrent local perturbations, such as predation‐induced breeding failure, may lead individuals to disperse locally or regionally. This might induce heterogeneity in individual CR histories and biases in demographic parameter estimates. Here, we assessed the effect of buffer areas and multi‐site designs on annual survival estimate accuracy and precision. First, using simulated data with and without a multi‐site design, we compared survival estimates for different scenarios involving contrasted local and regional dispersal. Then, using data from a local multi‐site long‐term monitoring survey carried out in a colony of black‐legged kittiwakes, we tested the homogeneity of individual CR histories and compared survival estimates from three datasets including one or several breeding cliffs with or without buffer areas. Results from simulations highlighted that a single‐site design consistently led to underestimated or less precise survival values compared to a multi‐site design. Similarly, a multi‐site design allowed detecting significant temporal trends while a single‐site one did not. In the field‐based study, adding buffer areas reduced individual heterogeneity, but all three datasets suffered from strong trap‐dependence possibly due to individual breeding success affecting nest‐site fidelity. Implementing a multi‐site design significantly improved the accuracy and precision of annual survival estimates, regardless of CR models applied. Adapting the spatial scale of sampling design to the local environment and species behaviour is essential to provide robust parameters of key relevance for population monitoring and management. We show that implementing buffer areas and/or multi‐site designs may be especially beneficial for long‐lived species facing regular local perturbation events leading to local dispersal.
DWELL: Design For Wellness - An Online Intervention To Create Healthier Home Environments
ClinicalTrials.gov study NCT03736525. IPD Sharing: NO. Countries: 1. Publications: 1.
Stroke Rehabilitation Utilizing Therapeutical Methods Designed for Nature Environments
ClinicalTrials.gov study NCT06633146. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Data from: Survival estimates strongly depend on capture-recapture designs in a disturbed environment inducing dispersal
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Design and Evaluation of Care Environment and Technology at Pediatric Radiotherapy
ClinicalTrials.gov study NCT02993978. IPD Sharing: NO. Countries: 0. Publications: 3.
Data from: Flower preferences and pollen transport networks for cavity nesting solitary bees: implications for the design of agri-environment schemes
Floral foraging resources are valuable for pollinator conservation on farmland, and their provision is encouraged by agri-environment schemes in many countries. Across Europe, wildflower seed mixtures are widely sown on farmland to encourage pollinators, but the extent to which key pollinator groups such as solitary bees exploit and benefit from these resources is unclear. We used high-throughput sequencing of 164 pollen samples extracted from the brood cells of 6 common cavity nesting solitary bee species (Osmia bicornis, Osmia caerulescens, Megachile versicolor, Megachile ligniseca, Megachile centuncularis and Hylaeus confususi) which are widely distributed across the UK and Europe. We documented their pollen use across 19 farms in southern England, UK, revealing their forage plants and examining the structure of their pollen transport networks. Of the 32 plant species included currently in sown wildflower mixes, 15 were recorded as present within close foraging range of the bees on the study farms, but only Ranunculus acris L. was identified within the pollen samples. Rosa canina L. was the most commonly found of the 23 plant species identified in the pollen samples, suggesting that, in addition to providing a nesting resource for Megachile leafcutter bees, it may be an important forage plant for these species. Higher levels of connectance and nestedness were characteristic of pollen transport networks on farms with abundant floral resources, which may increase resilience to species loss. Our data suggest that plant species promoted currently by agri-environment schemes are not optimal for solitary bee foraging. If a diverse community of pollinators is to be supported on UK and European farmland, additional species such as Rosa canina should be encouraged to meet the foraging requirements of solitary bees.
Data from: Flower preferences and pollen transport networks for cavity nesting solitary bees: implications for the design of agri-environment schemes
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A Scoping Literature Review on 'Immersive Virtual Environments as a Tool in Evidence-based Healthcare Facility Design
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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.