Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
997
datasets available to search
ShareScore release 0.9.0
Dataset results
997 results for “Awareness”
Learning Context-aware Structural Representations to Predict Antigen and Antibody Binding Interfaces
<p>These are data corresponding to the paper '<a href="https://doi.org/10.1093/bioinformatics/btaa263">Learning Context-aware Structural Representations to Predict Antigen and Antibody Binding Interfaces</a>', published in Bioinformatics 2020. The code to run the experiments can be found on github <a href="https://github.com/vamships/PECAN">here</a>.</p>
User-aware music auto-tagging with contextual tags
<p>This is a user-aware music dataset labeled with the contextual use of each track according to each user. The dataset is composed of 10 contextual tags extracted based on user's usage through created playlists in the Deezer catalog. The tags are: " car, gym, happy, night, relax, running, sad, summer, work, workout". For each track/user pair, a contextual tag is associated with it indicating that the user listens to the track in the associated context. Additionally, the users are represented as embeddings based on their listening history computed through the matrix factorization of the user/track matrix.</p> <p>The creation of the dataset and the baseline of our auto-tagging model is described in the paper: Karim M. Ibrahim, Elena V. Epure, Geoffroy Peeters, and Gaël Richard. "Should we consider the users in contextual music auto-tagging models?" <em>21st International Society for Music Information Retrieval Conference (ISMIR)</em>. 2020. The source code of the paper is available here: <a href="https://github.com/KarimMibrahim/user-aware-music-autotagging">https://github.com/KarimMibrahim/user-aware-music-autotagging</a></p> <p>The dataset is composed of the SONG_ID which is the ID of the track in the Deezer catalog. Each track/user pair is labeled with each tag as either 1 (indicating a track's presence in the context) or 0 (indicating a track's absence). The 30 seconds track previews used to train the model in the paper can be accessed through the Deezer API: <a href="https://developers.deezer.com/api">https://developers.deezer.com/api</a>. Each user is represented with an anonymized USER_ID which is associated with the user embedding available in the user_embeddings.csv file. </p>
Data from RACE: Resource Aware Cost-Efficient Scheduler for Cloud Fog Environment
<p>The quality of human life increases with increased usage of the Internet of Things (<i>IoT</i>). However, this massive use of IoT produces a large amount of data which creates a problem for data storage and analysis. Cloud computing is used to solve such issues like storage and processing requirements for data generated by IoT. However, several applications like traffic control, health observation, and games, etc., are time-sensitive and need a quick response. The delay created by sending data to the Cloud and then returning the Server response to the user of such programs has an unintended impact. To overwhelmed these limitations, the Fog computing concept was launched in 2012.</p> <p>Fog computing is an elongation of Cloud computing in which user services are extended to networking devices. In recent times the Fog computing is considered as a favorable approach to cope with the delay-sensitive applications. Moreover, Fog computing in combination with the Cloud computing model can also provide promising solutions to deal with compute-intensive applications. The scheduling challenges faced by the Fog service provider are to fulfill user requests with the maximum resource utilization of Fog devices while reducing the monetary cost of Cloud resource usage. In this research, a resource-aware scheduler has been proposed to distribute the incoming application modules to Fog devices that maximize resource utilization at the Fog layer, reduces the monetary cost of using Cloud resources with minimum execution time of applications, and minimum bandwidth usage.</p> <p>The proposed scheduler Resource Aware Cost-Efficient Scheduler (RACE) consists of two algorithms: ModuleScheduler: which categorizes the incoming application modules according to their computation and bandwidth requirements. The ModuleScheduler also creates a prioritized list considering the categories of application modules. Compare Module: Finds the suitable Fog device for modules execution in the prioritized list. The proposed scheduler (RACE) schedule the modules at the Fog layer based on their computation requirement and bandwidth requirement until the devices at the Fog layer have enough CPU capacity to accommodate the modules.</p>
The dataset of the paper titled "Context-Aware Code Change Embedding for Better Patch Correctness Assessment"
<p>The dataset of the paper titled "Context-Aware Code Change Embedding for Better Patch Correctness Assessment".</p> <p>This is the online repository of the paper "Context-Aware Code Change Embedding for Better Patch Correctness Assessment" under review by ASE2021. We release the source code of Cache, the patches used in our evaluation, as well as the experiment results.</p> <ul> <li> <p>Patches: Two patch benchmarks included in our study.</p> <ul> <li>Small: The 1,183 deduplicated patches from Tian's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/3/Evaluating-Representation-Learning-of-Code-Changes-for-Predicting-Patch-Correctness-i">ASE20</a> paper and Wang's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/54/Automated-Patch-Correctness-Assessment-How-Far-are-We-">ASE20</a> paper.</li> <li>Large: The patches collected by ourselves, which is consist of totally 49,694 patches from <a href="https://dl.acm.org/doi/10.1145/3338906.3338911">RepairThemAll</a> and <a href="https://dl.acm.org/doi/10.1145/3379597.3387491">ManySStuBs</a>.</li> </ul> </li> <li> <p>Results</p> <ul> <li> <p>RQ1: The detailed result files in RQ1, which are named by the format of <em><code>[model]_[classifier].csv</code></em>. For example, the file named <em><code>BERT_DT.csv</code></em> in the folder <em><code>Small</code></em> means that this file is the result of patches from <strong>Small</strong> dataset embedded by <strong>BERT</strong> and classified by <strong>Decision Tree</strong>.</p> <ul> <li>Small: The detailed result files on Small dataset.</li> <li>Large: The detailed result files on Large dataset.</li> <li> <p>Cross: The detailed result files of representation learning techniques when training on Large dataset and testing on Small dataset.</p> </li> </ul> </li> <li> <p>RQ2: The detailed result files in RQ2.</p> <ul> <li>Wang_Cache.csv: The detailed result of Cache on the dataset from Wang's <a href="https://conf.researchr.org/details/ase-2020/ase-2020-papers/54/Automated-Patch-Correctness-Assessment-How-Far-are-We-">ASE20</a> paper.</li> <li> <p>ODS_Cache.csv: The datailed result of Cache on the dataset from Xiong's <a href="https://dl.acm.org/doi/10.1145/3180155.3180182">ICSE18</a> paper. We directly compare against the results reported by the authors of ODS on 139 patches from Xiong's paper since the data and source code of ODS is unavailable.</p> </li> <li> <p>Table_5_Effectiveness_APCA.xlsx: The detailed version of <strong>Table 5</strong> in the paper.</p> </li> <li> <p>Table_6_Effectiveness_ODS.xlsx: The detailed version of <strong>Table 6</strong> in the paper.</p> </li> </ul> </li> </ul> </li> <li>Source: The source code and lib for running Cache. Guidance for replicating our study is available at <em><code>source/Readme.md</code></em>. We will build a homepage for Cache on GitHub upon acceptance.</li> </ul>
Investigating Transformers as Context-aware Word Search Engines - Data and Code
<p>This is the review version, please use the published record: </p> <p> https://zenodo.org/record/6425595</p> <p> </p> <p>Data and code for the paper 'Investigating Transformers as Context-aware Word Search Engines' as submitted for review at ACL21. </p> <p> </p>
Data from: "ABC" - the Awareness-Body-Chart: a New Tool Assessing Body Awareness
Background: Despite the importance of body awareness for health and well-being there is still a lack of valid assessment tools to scan proper body awareness. To respond to the limitations of questionnaires (reading/interpretation problems of subjects) the Awareness-Body-Chart (ABC) was designed to assess body awareness by colouring 51 regions according to their awareness. The objective of this study was to investigate the psychometric characteristics of the ABC. Methods: In a questionnaire-study, 106 students in Graz (79 females, 27 males, age median 21 (IQR 20 - 23) years) filled in the ABC, furthermore a German body awareness questionnaire "KEKS", and the Beck Depression Inventory II. Factor structure, internal consistency, and retest reliability of the ABC were investigated. Correlations of the ABC with the KEKS and the Beck Depression Inventory II and comparisons of subgroups were conducted. Results: Through factor analyses, 14 factors with clear assignments to body parts could be categorized: cranium, face, cervical/lumbar region, chest/abdomen, back, shoulder, upper arm, lower arm/elbow, hand, genital area, thigh/hip, knee, lower leg, and foot. The 14 body parts and the total score showed acceptable to high Cronbach's alphas (α = .64 - .97). The test-retest reliability showed values between ρ = .71 and ρ = .96. The correlation of the ABC and KEKS (r = .66, p < .001) confirmed validity. Further indications of validity could be seen in comparisons of subgroups and in correlations with the Beck Depression Inventory II. Conclusion: The ABC proved good psychometric properties with acceptable to high internal consistency, acceptable to high retest reliability and high construct validity. It is an easy-to-use tool for clinical settings and research. The ABC opens new insights into body awareness-patterns of various subgroups.
Data from: INSTRAL: discordance-aware phylogenetic placement using quartet scores
Phylogenomic analyses have increasingly adopted species tree reconstruction using methods that account for gene tree discordance using pipelines that require both human effort and computational resources. As the number of available genomes continues to increase, a new problem is facing researchers. Once more species become available, they have to repeat the whole process from the beginning because updating species trees is currently not possible. However, the de novo inference can be prohibitively costly in human effort or machine time. In this paper, we introduce INSTRAL, a method that extends ASTRAL to enable phylogenetic placement. INSTRAL is designed to place a new species on an existing species tree after sequences from the new species have already been added to gene trees; thus, INSTRAL is complementary to existing placement methods that update gene trees.
Data from: Catch the wave: prairie dogs assess neighbours' awareness using contagious displays
The jump–yip display of black-tailed prairie dogs (Cynomys ludovicianus) is contagious, spreading through a prairie dog town as 'the wave' through a stadium. Because contagious communication in primates serves to assess conspecific social awareness, we investigated whether instigators of jump–yip bouts adjusted their behaviour relative to the response of conspecifics recruited to display bouts. Increased responsiveness of neighbouring town members resulted in bout initiators devoting a significantly greater proportion of time to active foraging. Contagious jump–yips thus function to assess neighbours' alertness, soliciting social information to assess effective conspecific group size in real time and reveal active probing of conspecific awareness consistent with theory of mind in these group-living rodents.
Data from: Prevalence, associated factors, awareness, treatment, and control of hypertension: findings from a cross sectional study conducted as a part of a community based intervention trial in Surkhet, Mid-western region of Nepal
Background Hypertension is one of the leading public health problems globally. About half of the deaths from cardiovascular diseases were attributed to hypertension in 2008. Reduction of blood pressure to normal range is one of the major challenges in preventing complications and future burden of cardiovascular diseases. Therefore, this study aims to determine prevalence, awareness, treatment and control of hypertension and its associated factors in Nepal. Methods This was a community based cross-sectional study conducted as a part of a community based intervention trial in Birendranagar Municipality of Surkhet district located at the Mid-western region of Nepal. We enrolled 1159 subjects aged 30 years and above. Out of 12 wards (administrative unit), four wards were selected randomly. Three hundred participants were recruited from each selected ward. Trained enumerator collected socio-demographic, anthropometric, and clinical data using standard STEPS questionnaires. Results Out of all participants, women were 71% and mean age was 47±12.6 years. The overall prevalence of hypertension was 38.9% (95% CI: 36–41.7) while age and sex adjusted prevalence was 40.6%. The hypertension was present in 48.1% (95% CI: 45.2–50.9) of men and 35.2% (95% CI: 32.4–37.9)] of women. Male gender (OR = 1.49), older age (OR = 1.04 per year), Dalit caste (OR = 1.71), past history of cigarettes smoking (OR = 2.78), current alcohol consumption (OR = 1.75), and raised body mass index (OR = 1.17 per unit) were identified as significant factors associated with hypertension. Of total hypertensive respondents, 53.4% (95% CI: 48.7–58) were aware, 29% (95% CI: 24.8–33.1) were receiving treatment for high blood pressure, and 8.2% (95% CI: 5.6–10.7) had controlled blood pressure. The awareness, treatment, and control status were worse in younger participants. Conclusions The study revealed high prevalence with low awareness, treatment, and control of hypertension in Nepal. Gender, age, ethnicity, smoking, drinking alcohol, and body mass index were associated with hypertension. Immediate public health and individual measures are warranted to reduce future burden of cardiovascular diseases.
An Architecture-aware Approach to Hierarchical Online Failure Prediction
<p>Supplementary material for the paper:</p> <p>"An Architecture-aware Approach to Hierarchical Online Failure Prediction".</p> <p>Included in the supplementary material are:</p> <ul> <li>Architectural Dependency Model (ADM): the ADM used in the evaluation (exported to yml format).</li> <li>Failure Propagation Model (FPM): the structure of the FPM and the corresponding conditional probability tables.</li> <li>Proof-of-concept implementation of Hora, including instruction of how to build, run, and analyze the results. Note: Hora currently works only on Linux due to a limitation in R bridge for Windows.</li> <li>Evaluation results of two failure scenarios; memory leak, system overload and the overall result.</li> </ul> <p>Additionally, the following datasets used in the experiment are also provided:</p> <ul> <li>35-SystemOverload.zip</li> <li>36-MemoryLeak.zip</li> </ul>
Type-Aware schedules and parameters
<p>Schedules and power/performance parameters for the article "Workload Type-Aware Scheduling on big.LITTLE Platforms"</p>
Hora: Architecture-aware online failure prediction
<p>Complex software systems experience failures at runtime even though a lot of effort is put into the development and operation. Reactive approaches detect these failures after they have occurred and already caused serious consequences. In order to execute proactive actions, the goal of online failure prediction is to detect these failures in advance by monitoring the quality of service or the system events. Current failure prediction approaches look at the system or individual components as a monolith without considering the architecture of the system. They disregard the fact that the failure in one component can propagate through the system and cause problems in other components. In this paper, we propose a hierarchical online failure prediction approach, called HORA , which combines component failure predictors with architectural knowledge. The failure propagation is modeled using Bayesian networks which incorporate both prediction results and component dependencies extracted from the architectural models. Our approach is evaluated using Netflix’s server-side distributed RSS reader application to predict failures caused by three representative types of faults: memory leak, system overload, and sudden node crash. We compare HORA to a monolithic approach and the results show that our approach can improve the area under the ROC curve by 9.9%.</p>
Technical Debt Awareness: Definitions, Methods, and Metrics
<p>The additional material for the paper "Technical Debt Awareness: Definitions, Methods, and Metrics" comprises the following documents:</p><p> </p><p>* Guideline_to_Use_TD-SAGAT.docx (.pdf)</p><p> A short guideline on how the use the TD-SAGAT method.</p><p>* Semi_SLR_papers.xlsx</p><p> All excluded and included papers of our smei-SLR whit resons for exclusion and extracted data for included papers.</p><p>* Participant_and_Meetings.xslx</p><p> overview of all participants with role and years of experience </p><p> overview of all meetings, we used for analysis</p><p>* Survey_and_Observation_Template_plus_Mappings.xlsx</p><p> survey questions for workshop survey, TD-SAGAT survey and observation protocol</p><p> mapping of all questions and aspects between the surveys and the obersvation aspects</p><p>* SAGAT_for_TD_from_Goals_to_Queries.pdf</p><p> Determination of survey questions by following the SAGAT approach of following the hierarchy of goals, subgoals, decisions, requirements, queries </p><p>* TD-SAGAT_Analysis.xlsx</p><p> analysis data for the TD-SAGAT analysis, incl. chart</p><p>* Observations.xslx</p><p> observation protocols (anonymized) for all observed meetings</p><p> </p>
GAABind: A Geometry-Aware Attention-Based Network for Accurate Protein-Ligand Binding Pose and Binding Affinity Prediction
<p>The preprocessed dataset for paper "GAABind: A Geometry-Aware Attention-Based Network for Accurate Protein-Ligand Binding Pose and Binding Affinity Prediction" with associated code at https://github.com/Mercuryhs/GAABind.</p><p>The dataset files are saved as .pkl file for the convenience of use.</p><p><strong>Paper Abstract</strong>:</p><p>Protein-ligand interactions are increasingly profiled at high-throughput, playing a vital role in lead compound discovery and drug optimization. Accurate prediction of binding pose and binding affinity constitutes a pivotal challenge in advancing our computational understanding of protein-ligand interactions. However, inherent limitations still exist, including high computational cost for conformational search sampling in traditional molecular docking tools, and the unsatisfactory molecular representation learning and intermolecular interaction modeling in deep learning-based methods. Here we propose a geometry-aware attention-based deep learning model, GAABind, which effectively predicts the pocket- ligand binding pose and binding affinity within a multi-task learning framework. Specifically, GAABind comprehensively captures the geometric and topological properties of both binding pockets and ligands, and employs expressive molecular representation learning to model intramolecular interactions. Moreover, GAABind proficiently learns the intermolecular many-body interactions and simulates the dynamic conformational adaptations of the ligand during its interaction with the protein through meticulously designed networks. We trained GAABind on the PDBbindv2020 and evaluated it on the CASF2016 dataset, the results indicate that GAABind achieves state-of-the-art performance in binding pose prediction and shows comparable binding affinity prediction performance. Notably, GAABind achieves a success rate of 82.8% in binding pose prediction, and the Pearson correlation between predicted and experimental binding affinities reaches up to 0.803. Additionally, we assessed GAABind's performance on the SARS-CoV-2 main protease cross-docking dataset. In this evaluation, GAABind demonstrates a notable success rate of 76.5% in binding pose prediction and achieves the highest Pearson correlation coefficient in binding affinity prediction compared with all baseline methods.</p>
Awareness of Lung Cancer Risk Factors and Symptoms in Syria: An Online Cross-Sectional Study
<p><strong>Background:</strong> Globally, lung cancer is the leading cause of cancer fatalities and the second most frequent cancer. Population knowledge of the features of lung cancer is a crucial strategy for early diagnosis and decreasing the mortality rate of lung cancer patients. In this study, we aim to assess the Syrian population's knowledge of lung cancer and its risk factors and to measure awareness of symptoms related to lung cancer. <strong>Methods:</strong> This national cross-sectional study was conducted between October 12 to November 21, 2022 in Syria. We included Syrian people above 18 years from all Syrian governorates. The questionnaire consisted of three categories of questions: sociodemographic information, awareness of lung cancer symptoms, and awareness of lung cancer risk factors. <strong>Results:</strong> Overall, 2251 participants were involved in this research; almost half of them (47.3%) were aged between 21-30 years, and 30.9% indicated they are smoking cigarettes. The overall mean score of knowledge regarding closed questions about risk factors of lung cancer was 4.29; however, the mean score of knowledge regarding open questions about symptoms of lung cancer was 1.52. About half of the study sample (51.3%) indicated that unexplained weight loss is a possible symptom of lung cancer. Our findings showed that cigarette smokers have a lower probability of having adequate knowledge toward lung cancer risk factors than a non-smoker (AOR=0.73, COR=0.68, P-value<0.05). We also defined that females have higher statistically significant odds (AOR=1.3, COR=1.38, P-value<0.05) for being knowledgeable about the symptoms of lung cancer compared to the male sample study. Conclusion: According to our findings, there is inadequate knowledge toward lung cancer risk factors and moderate knowledge of lung cancer symptoms. Along with educational programs to raise public knowledge of the dangers of smoking and other LC risk factors, effective tobacco control policy execution is crucial. </p>
STUDY OF INVESTMENT AWARENESS AMONG WORKING WOMEN IN BHOPAL
<p>Every women investor has a unique choice of investment option based on her awareness of investment option. Every investment option has its own benefit. This research paper is focusing on working women of Bhopal district. In Bhopal women are involved in various jobs like banks, educational institute, corporates art workers, farming etc. in fact now a day women are more involved in their own business. As we all know that role of women is changing now a days from a house wife to business women or entrepreneurs. Since the last 10 to 15 years, this situation has been changing. This study aims to focus on investment awareness and sources of information available to working women with special reference to Bhopal district, the study is based on female employees only who are working in government and private sector organizations. For the purpose of this study primary data collected through questionnaire method using convenient sampling technique with the sample size of 150 respondents of Bhopal district, data analysed with percentage method and Annova technique</p>
RavenBuild: Context, Relevance, and Dependency Aware Build Outcome Prediction
<p>Replication and raw data of RavenBuild</p>
QG energy-aware hybrid simulations (Double-Gyre configuration)
<p>QG energy-aware hybrid simulations for different set of parameters and compensating forcing; reference QG solution and the coarse-grid runs.</p> <p>There are five different groups of data files containing quasi-geostrophic energy-aware hybrid simulations for different set of parameters and compensating forcing; <br>reference quasi-geostrophic solution and the coarse-grid runs:</p> <p>1) 129x129_10y_spinup50y_dt1800s_simtime2y_eta0.02_case2.tar.part*<br>2) 29x129_10y_spinup50y_dt1800s_simtime2y_eta0.02_fast_energy_growth.tar.part*<br>3) 129x129_10y_spinup50y_dt1800s_simtime2y_eta0.02_scales2.tar.part*<br>4) 129x129_2y_spinup50y_dt1800s.tar.part*<br>5) 129x129_2y_spinup50y_dt1800s_refsol.tar.part*</p> <p>All files in the repository are tar-archives split into parts. Before using them, they should be merged into single files. In order to merge them one should use the Linux cat command. The data in the merged tar-files can be then extracted with the Linux tar command.</p>
Open Data Package: Lessons Learned from Developing a Sustainability Awareness Framework for Software Engineering Using Design Science.
<p>Open Data Package for the paper: Stefanie Betz, Birgit Penzenstadler, Leticia Duboc, Ruzanna Chitchyan, Sedef Akinli Kocak, Ian Brooks, Shola Oyedeji, Jari Porras, Norbert Seyff, and Colin C. Venters. 2024. Lessons Learned from Developing a Sustainability Awareness Framework for Software Engineering Using Design Science. ACM Trans. Softw. Eng. Methodol. 24 00, JA, Article 00 (March 2024), 39 pages. https://doi.org/10.1145/3649597 25</p>
Change-Aware Sampling and Contrastive Learning for Satellite Images
<p><strong>Instructions</strong></p> <div>Change-Aware Sampling and Contrastive Learning for Satellite Images</div> <div>The 1 million sized dataset in compressed format.</div> <div>This dataset is split in 4 parts due to Zenodo's size restictions.</div> <div>Each part can be downloaded using the following link.</div> <p><strong>Part 1: </strong><a href="../records/10913216">https://zenodo.org/records/10913216</a></p> <p><strong>Part 2: </strong><a href="../records/10914902">https://zenodo.org/records/10914902</a></p> <p><strong>Part 3: </strong><a href="10915715">https://zenodo.org/uploads/10915715</a></p> <p><strong>Part 4: </strong><a href="../records/10916979">https://zenodo.org/records/10916979</a></p> <p> </p> <div>Use the following commands to combine and extract the compressed file.</div> <blockquote> <div>cat clean_1m_geography_part* > clean_1m_geography.tar.gz</div> <div>tar -xvf clean_1m_geography.tar.gz</div> </blockquote> <p> </p> <p><strong>Paper Abstract</strong></p> <p>Automatic remote sensing tools can help inform many large-scale challenges such as disaster management, climate change, etc. While a vast amount of spatio-temporal satellite image data is readily available, most of it remains unlabelled. Without labels, this data is not very useful for supervised learning algorithms. Self-supervised learning instead provides a way to learn effective representations for various downstream tasks without labels. In this work, we leverage characteristics unique to satellite images to learn better self-supervised features. Specifically, we use the temporal signal to contrast images with long-term and short-term differences, and we leverage the fact that satellite images do not change frequently. Using these characteristics, we formulate a new loss contrastive loss called Change-Aware Contrastive (CACo) Loss. Further, we also present a novel method of sampling different geographical regions. We show that leveraging these properties leads to better performance on diverse downstream tasks. For example, we see a 6.5% relative improvement for semantic segmentation and an 8.5% relative improvement for change detection over the best-performing baseline with our method.<br><br><br></p> <div> <div> </div> </div>
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.