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82 results for “Adaptive Learning”
Dataset: Reinforcing Cybersecurity Hands-on Training With Adaptive Learning
<p>This repository contains supplementary materials for the following conference paper:<br> <br> Pavel Seda, Jan Vykopal, Valdemar Švábenský, Pavel Čeleda.<em><br> Reinforcing Cybersecurity Hands-on Training With Adaptive Learning. </em><br> In Proceedings of the 51st IEEE Frontiers in Education Conference (FIE 2021).<br> <a href="https://doi.org/10.1109/FIE49875.2021.9637252">https://doi.org/10.1109/FIE49875.2021.9637252</a><br> <br> Preprint available at: <a href="https://arxiv.org/abs/2201.01574">https://arxiv.org/abs/2201.01574</a></p> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).</p> <p>Some of the linked repositories have their separate citation entry; please use that one as well, if possible.</p> <pre><code>@inproceedings{Seda2021reinforcing, author = {Seda, Pavel and Vykopal, Jan and \v{S}v\'{a}bensk\'{y}, Valdemar and \v{C}eleda, Pavel}, title = {{Reinforcing Cybersecurity Hands-on Training With Adaptive Learning}}, booktitle = {Proceedings of the 51st IEEE Frontiers in Education Conference}, series = {FIE '21}, location = {Lincoln, NE, USA}, publisher = {IEEE}, address = {New York, NY, USA}, month = {10}, year = {2021}, pages = {1--9}, numpages = {9}, isbn = {978-1-6654-3851-3}, url = {https://doi.org/10.1109/FIE49875.2021.9637252}, doi = {10.1109/FIE49875.2021.9637252}, }</code></pre> <p> </p>
SMART - Self-adaptive Machine Learning Approach for Real-time Tuning of IEEE 802.11 PHY and MAC layers
<p><strong>Introduction</strong></p> <p>Worldwide the demand for wireless access networks providing very high throughputs has been increasing exponentially, namely due to bandwidth-hungry applications such as high definition video streaming and augmented reality. In order to fulfil these requirements, the Wi-Fi standard was enriched with new amendments, such as IEEE 802.11n, IEEE 802.11ac, and recently IEEE 802.11ax (Wi-Fi 6). New parameters have been proposed for both physical (PHY) and media access control (MAC) layers, including channel bonding, short guard interval (SGI), and advanced modulation and coding schemes (MCS).</p> <p>However, the high variability of the signal strength in the wireless radio channel, allied to the channel asymmetry, makes the selection of optimal configurations for these parameters a challenge. Typically, these parameters are configured with a default value. For runtime optimization, some algorithms have already been proposed. Still, they were designed considering legacy IEEE 802.11 releases and static scenarios. Besides, these parameters have their trade-offs that need to be properly managed. To help dealing with this, machine learning has been recently introduced in wireless networks, providing the intelligence that networks need in order to be smart and self-adaptive.</p> <p>SWOP (Smart Wireless Optimization) is a cross-layer optimization approach for Wi-Fi networks extending the current Rate Adaptation (RA) approach, for instance, followed by the well-known Minstrel algorithm widely used in practice. Our approach takes advantage of Deep Reinforcement Learning (DRL) in order to learn the optimal Wi-Fi link configuration. By considering the wireless channel as the environment, the transmitter node (the agent) chooses the best link parameters (the action) in order to maximize the throughput (the reward) based on the channel metrics captured from the environment (the state). In this work we propose a simple DRL-based Wi-Fi Rate Adaptation (RA) algorithm, named Data-driven Algorithm for Rate Adaptation (DARA), which is one of the modules of Smart Wireless Optimization (SWOP)</p> <p>SMART aimed to run a set of wireless experiments on top of w-iLab.t testbeds provided by the Fed4FIRE+ project to directly validate our DRL model and learn a policy from the wireless experiments executed in a controlled environment. However, after facing difficulties with the scenarios we could achieve on the real testbed, we decided to train and test DARA using a trace-based simulation approach. In simulation, we could train our model in scenarios that are more complex and diverse whilst easy to configure, when compared to real testbeds. The w-iLab.t testbeds were still used to capture data traces (e.g. Signal-to-Noise Ratio, position of nodes, transmission power and link distance) that were then injected in ns-3 for validating DARA.</p> <p>With this work, we concluded that DARA performance is impacted when operating in scenarios with asymmetric links, which is common in the highly dynamic and unpredictable wireless environments. Furthermore, the asymmetry offset varies between scenarios and it may also change for the same scenario, as time progresses. This randomness is not addressed when solely considering the SNR as the link metric, posing a challenge in the learning phase of DARA. Despite these limitations, the results obtained show that DARA still achieves up to 14.9% higher throughput higher than Minstrel [1] and slightly lower than Ideal [2] for most of the scenarios. The results obtained will serve as a basis to support our ongoing and future research.</p> <p> </p> <p><strong>Folder Organization</strong></p> <p>The following dataset presents the results of the SMART project, organized in different folders for each Rate Adaptation Algorithm, as well as the traces that were used to obtain such results:</p> <ul> <li><strong>DARA: </strong>Results obtained using our solution <strong>(Naming Convention #1, Folder Content #1)</strong></li> <li><strong>MIN: </strong>Results obtained using Minstrel-HT <strong>(Naming Convention #1, Folder Content #2)</strong></li> <li><strong>ID: </strong>Results obtained using Ideal <strong>(Naming Convention #1, Folder Content #2)</strong></li> <li><strong>TRACES: </strong>Trace files used to obtain the results present in this dataset <strong>(Naming Convention #2, Folder Content #3)</strong></li> </ul> <p><strong>Naming Convention #1 – RAA TID TP TO:</strong></p> <ul> <li>Rate Adaptation Algorithm<strong> (RAA) </strong> <ul> <li><strong>drl </strong>– Data Driven Algorithm for Rate Adaptation</li> <li><strong>min </strong>– MinstrelHTWifiManager</li> <li><strong>id </strong>– IdealWifiManager</li> </ul> </li> <li>Trace ID<strong> (TID)</strong> <ul> <li><strong>3 </strong>up to<strong> 8</strong></li> </ul> </li> <li>Transport Protocol<strong> (TP)</strong> <ul> <li><strong>udp </strong>– User Datagram Protocol</li> </ul> </li> <li>Traffic Orientation<strong> (TO)</strong> <ul> <li><strong>normal </strong>– A<strong>-></strong>B</li> <li><strong>reversed </strong>– B<strong>-></strong>A</li> </ul> </li> </ul> <p><strong>Naming Convention #2 – TID_TXP:</strong></p> <ul> <li>Trace ID<strong> (TID) </strong> <ul> <li><strong>3 </strong>up to<strong> 8</strong></li> </ul> </li> <li>Transmitting Power in dBm <strong>(TXP) </strong> <ul> <li><strong>3, 5, 7, 9, 12 dBm </strong></li> </ul> </li> </ul> <p><strong>Folder Content #1: </strong></p> <ul> <li><em>checkpoint_ RAA TID TP TO</em><strong> (Folder)</strong> <ul> <li><strong>Policy Checkpoint</strong> with which the results were obtained</li> </ul> </li> <li> <ul> <li><strong>Flowmonitor </strong>output for the configured scenario</li> </ul> </li> <li> <ul> <li>Column 1 – <strong>Step Counter</strong></li> <li>Column 2 – <strong>Reward Value</strong></li> <li>Column 3 – <strong>Observation Value</strong></li> <li>Column 4 – <strong>Action Value</strong></li> </ul> </li> <li> <ul> <li>Column 1 – <strong>Simulation Time </strong>(seconds)</li> <li>Column 2 – <strong>Throughput </strong>(Mbit/100ms)</li> </ul> </li> </ul> <p><strong>Folder Content #2: </strong></p> <ul> <li> <ul> <li><strong>Flowmonitor </strong>output for the configured scenario</li> </ul> </li> <li> <ul> <li>Column 1 – <strong>Simulation Time </strong>(seconds)</li> <li>Column 2 – <strong>Throughput </strong>(Mbit/100ms)</li> </ul> </li> </ul> <p><strong>Folder Content #3 - </strong>Source: <a href="https://zenodo.org/record/3713271#.YjjBVDXLdhE">https://zenodo.org/record/3713271#.YjjBVDXLdhE</a><strong>: </strong></p> <p>· <em>date_time</em><strong>.cfg </strong>configuration details of the experiment</p> <p>· <em>date_time_NodeID</em><a href="https://zenodo.org/record/3713271#_ftn1"><strong><em><sup>[1]</sup></em></strong></a><em>_SenderID</em><a href="https://zenodo.org/record/3713271#_ftn2"><strong><em><sup>[2]</sup></em></strong></a><em>_ReceiverID</em><a href="https://zenodo.org/record/3713271#_ftn3"><strong><em><sup>[3]</sup></em></strong></a><em>_FlowType</em><a href="https://zenodo.org/record/3713271#_ftn4"><strong><em><sup>[4]</sup></em></strong></a><em>_Params</em><a href="https://zenodo.org/record/3713271#_ftn5"><strong><em><sup>[5]</sup></em></strong></a><strong>.snr </strong>– logs of the Signal/Noise ratio (1 file per node/flow) </p> <p>· <em>date_time_NodeID_SenderID_ReceiverID_FlowType_Params</em><strong>.stats</strong> – logs of the packets received (1 file per node/flow) </p> <p><a href="https://zenodo.org/record/3713271#_ftnref1"><sub>[1]</sub></a><sub> ID of the node Logging node</sub></p> <p><a href="https://zenodo.org/record/3713271#_ftnref2"><sub>[2]</sub></a><sub> ID of the Sender node</sub></p> <p><a href="https://zenodo.org/record/3713271#_ftnref3"><sub>[3]</sub></a><sub> ID of the Receiver node</sub></p> <p><a href="https://zenodo.org/record/3713271#_ftnref4"><sub>[4]</sub></a><sub> Flow type: Unidirectional, Bidirectional or Unidirectional with Multiple Access</sub></p> <p><a href="https://zenodo.org/record/3713271#_ftnref5"><sub>[5]</sub></a><sub> Configurable parameters: Sender/Receiver Transmission Power and Data Rate (when applicable)</sub></p> <p> </p> <p><strong>References</strong></p> <p>1. F. FietKau, “Minstrel_HT: New rate control module for 802.11n [LWN.net]”. Mrt-2010.</p> <p>2. “ns-3: ns3::IdealWifiManager Class Reference,” Jan 2021, [Online; accessed 23. Jun. 2021]. Available: <a href="https://www.nsnam.org/docs/release/3.33/doxygen/classns3_1_1_ideal_wifi%20manager.html">https://www.nsnam.org/docs/release/3.33/doxygen/classns3_1_1_ideal_wifi manager.html</a></p>
Supplementary run files for the paper "Learning Effective Representations for Retrieval using Self-Distillation with Adaptive Relevance Margins"
<p>TREC-Format run files of all trained models as supplementary material for the paper "Learning Effective Representations for Retrieval using Self-Distillation with Adaptive Relevance Margins".</p> <p>File naming follows the schema: <code>{model}-{loss variant}-{in-batch usage}-{dataset}.txt.gz</code></p>
Data for Analysis for "A Framework for Adapting Conversational Intelligent Tutoring Systems to enable Collaborative Learning"
<p>This dataset includes, the data files for validating the statistical analysis from "A Framework for Adapting Conversational Intelligent Tutoring Systems to enable Collaborative Learning"</p> <p> </p> <p>The dataset is composed of 1500 files named following the pattern `Test-R-N-User-C-P.csv` where</p> <ul> <li>R is the n-th repetition. From 0 to 50</li> <li>N is the number of concurrent users. From 100 to 1000</li> <li>C is the treatment. chat for the framework version. chat-session for the legacy version.</li> <li>P is the problem number. 16 or 352.</li> </ul> <p>The data files corresponding to chat and problem 16 are those that in the paper are identified as Framework. The files por problem 352 are the collaborative version with students grouped.</p> <p>Each csv, is composed following the standard formate by Apache JMeter, and contains XX columns:</p> <ul> <li>timeStamp - UNIX timestamp of the request</li> <li>elapsed - Time taken to finish the request</li> <li>label - which step</li> <li>responseCode - HTTP response code</li> <li>responseMessage</li> <li>threadName</li> <li>dataType</li> <li>success - true|false</li> <li>failureMessage</li> <li>sentBytes</li> <li>grpThreads</li> <li>allThreads - Threads running</li> <li>URL - Endpoint URL</li> <li>Latency</li> <li>SampleCount</li> <li>ErrorCount - Cumulative amount of errors</li> <li>IdleTime </li> <li>Connect - Connection time</li> </ul> <p> </p>
Interdisciplinary Composition of E-Learning Platforms based on Reusable Low-Code Adapters
<p>E-learning platforms are becoming increasingly popular and, accordingly, are also being used more and more by teachers at schools and professors at universities. They are used to distribute educational material digitally to students, but also to offer the possibility to upload and collect assignments, solve tasks, and view grades. This thesis addresses the problems of the inflexibility of established platforms, assists lecturers in designing their courses, and motivates and supports students in their learning. Under the aspect of generalization, a concept for a software product line for the demands of various fields of study is designed, which provides lecturers with a basic platform and allows them to use low-code adapters to design and modify their courses according to their needs.</p>
Adaptive processing and perceptual learning in visual cortical areas V1 and V4
<p>Neurons in visual cortical areas primary visual cortex (V1) and V4 are adaptive processors, influenced by perceptual task. This is reflected in their ability to segment the visual scene into task-relevant and task-irrelevant stimulus components and by changing their tuning to task-relevant stimulus properties according to the current top-down instruction. Differences between the information represented in each area were seen. While V1 represented detailed stimulus characteristics, V4 filtered the input from V1 to carry the binary information required for the two-alternative judgement task. Neurons in V1 were activated at locations where the behaviorally relevant stimulus was placed well outside the grating-mapped receptive field. By systematically following the development of the task-dependent signals over the course of perceptual learning, we found that neuronal selectivity for task-relevant information was initially seen in V4 and, over a period of weeks, subsequently in V1. Once the learned information was represented in V1, on any given trial, task-relevant information appeared initially in V1 responses, followed by a 12-ms delay in V4. We propose that the shifting representation of learned information constitutes a mechanism for systems consolidation of memory.</p>
Dataset for: Adapting Explainable Machine Learning to Study Mechanical Properties of Two-Dimensional Hybrid Halide Perovskites
<p>This archive contains the in plane and out of plane Young's moduli (complete with respective VASP in and outputs) for 154 n=1 and 30 n>1 2D hybrid organic and inorganic perovskites. The data was used in the publication "Adapting Explainable Machine Learning to Study Mechanical Properties of Two-Dimensional Hybrid Halide Perovskites".</p> <p>Computational settings for the calculations were:</p> <p>Perdew-Burke-Ernzerhof (PBE) exchange-correlation with Tkatchenko-Scheffler (TS) van der Waals (vdW) corrections<br>Projector augmented-wave (PAW) method for the description of interactions between core and valence electrons.<br>A plane wave cutoff energy of 520 eV<br>A Γ-centered Monkhorst-Pack k-point mesh with a grid spacing of 2π × 0.040 Å−1 <br>Geometry optimizations were performed until energy and residual forces fell below 10−6 eV and 0.001 eV/ Å, respectively. <br><br></p>
Improving Open Source Face Detection by Combining an Adapted Cascade Classification Pipeline and Active Learning
<p>The <em><strong>EAVISE Open Source Face Detection Dataset</strong></em> consists of several items that were used to generate the improved frontal face detection model using LBP features and AdaBoost for OpenCV3.2.</p> <ul> <li>The annotations of the FDDB dataset, converted to the OpenCV format for doing a correct evaluation.</li> <li>The final trained model (IterativeHardPositives+ model) which is included in the OpenCV 3.2 framework.</li> </ul>
Global perspectives on sand dune patterns: Scale-adaptable classification using Landsat imagery and deep learning strategies
<p><span>Here we generated the global sand dune pattern map at a resolution of 30 m, named GSDP30. The GSDP30 map encompasses 11 types of sand dune patterns (SDPs): simple crescentic dunes, compound-complex crescentic dunes, simple linear dunes, compound-complex linear dunes, dome dunes, star dunes, parabolic dunes, dendritic dunes, network dunes, sand sheets, and others. The map is divided into 331 Tiff tiles, each characterized by a size of 15,360 × 15,360 pixels and named according to the longitude and latitude coordinates of its upper-left corner.</span></p>
Output Data for "Effective adaptation options to alleviate nuisance flooding in coastal megacities – learning from Ho Chi Minh City, Vietnam"
<p>This data repository pertains to the research article "Effective adaptation options to alleviate nuisance flooding in coastal megacities - learning from Ho Chi Minh City, Vietnam" by Leon Scheiber, Nivedita Sairam, Mazen Hoballah Jalloul, Kasra Rafiezadeh Shahi, Christian Jordan, Jan Visscher, Tara Evaz Zadeh, Laurens J.N. Oostwegel, Danijel Schorlemmer, Ngo Thanh Son, Hong Nguyen Quan, Torsten Schlurmann, Matthias Garschagen, Heidi Kreibich, published in Earth’s Future, 2024.</p> <p>The provided output data comprise:<br>- Risk Components (incl. inundation depths, exposed households and building values as well as relative losses)<br>- Expected Annual Damage (incl. 25/50/75th percentiles)<br>- Annually Affected Households </p> <p> </p>
Learning How to Search: Generating Effective Test Cases Through Adaptive Fitness Function Selection
<p>Data Package for "Learning How to Search: Generating Effective Test Cases Through Adaptive Fitness Function Selection"</p> <p>This package contains data generated as part of our experiments on adaptive fitness function selection as part of unit test generation for Java systems.</p> <p>This paper is currently under submission. A draft of the paper is included in the data package.</p> <p>This package contains experimental data (in folder "experiment_data"), including goal attainment, fault detection, time per generation, and choices made by the reinforcement learning algorithm. In the folder "test_suites", the suites generated by each technique are included for each project. </p> <p>If you have questions, please contact Gregory Gay at greg@greggay.com.</p> <p>NOTE: A small number of items are currently missing from this data package and will be added shortly. Please make sure you have the latest version of this package.</p>
Deep Reinforcement Learning for Data-Driven Adaptive Scanning in Ptychography
<p>These are the data sets used for the publication https://arxiv.org/abs/2203.15413.</p> <p>In more detail, the DataFile.pkl files in the training_data and testing_data folders are the actual data, including diffraction patterns, the corresponding reconstructions and the used illumination probe. The folders dwcnt_training_data and dwcnt_testing_data include the data sets of the simulated double-walled carbon nanotube.</p> <p>The reconstruction folder includes the source code of the reconstruction algorithm ROP and the used parameter files.</p> <p>The results folder includes the updated weights of the network model and the reconstructed potentials for the 25 test data sets used in the comparison of the publication.</p> <p>The sequence_grid.npy file stores the sparse grid scanning sequence used for initialization and comparison as described in the publication. The dwcnt_sequence_grid.npy file is the corresponding file for the double-walled carbon nanotube data.</p> <p>If you want more information, please contact the corresponding author of the publication, at schlozma@hu-berlin.de. </p>
Adaptive processing and perceptual learning in visual cortical areas V1 and V4
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Data from: Leveraging data mining, active learning, and domain adaptation for efficient discovery of advanced oxygen evolution electrocatalysts
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Social learning by mate-choice copying increases dispersal and reduces local adaptation
<p class="MsoPlainText">1. In heterogeneous environments, dispersal may be hampered not only by direct costs, but also because immigrants may be locally maladapted. While maladaptation affects both sexes, this cost may be modulated in females if they express mate preferences that are either adaptive or maladaptive in the new local population.</p> <p class="MsoPlainText">2. Dispersal costs under local adaptation may be mitigated if it is possible to switch to expressing traits of locally adapted residents. In a sexual selection context, immigrant females may learn to mate with locally favoured males. Mate-choice copying is a type of social learning, where individuals, usually females, update their mating preferences after observing others mate. If it allows immigrant females to switch from maladapted to locally adapted preferences, their dispersal costs are mitigated as mate choice helps them create locally adapted offspring.</p> <p class="MsoPlainText">3. To study if copying can promote the evolution of dispersal, we created an individual-based model to simulate the coevolution of four traits: copying, dispersal, a trait relevant for local adaptation, and female preference. We contrast two scenarios with copying — either unconditional, or conditional such that only dispersers copy — with a control scenario that lacks any copying.</p> <p class="MsoPlainText">4. We show copying to lead to higher dispersal, especially if copying is conditionally expressed. This leads to an increase in gene flow between patches and, consequently, a decrease in local adaptation and trait-preference correlations.</p> <p class="MsoPlainText">5. While our study is phrased with female preference as the learned trait, one may generally expect social learning to mitigate dispersal costs, with consequent feedback effects on the spatial dynamics of adaptation.</p>
Supplemental data to AALE 2024 publication "Adaptive manufacturing: dynamic resource allocation using multi-agent reinforcement learning"
<p>Release as supplementary material for our contribution at AALE 2024: "Adaptive manufacturing: dynamic resource allocation using multi-agent reinforcement learning"<br><br>The evaluation datasets stored in this collection are used to compare the performance of multi-agent reinforcement learning. In addition, the performance of other methods such as (meta-) heuristic algorithms or single agent reinforcement learning algorithms or novel methods of search space reduction can also be compared.</p>
DAEMON: Network intelligence for aDAptive and sElf-Learning MObile Networks
<p>The DAEMON H2020 european project develops and implements innovative and pragmatic approaches to Network Intelligence (NI) design that enable high performance, sustainable and extremely reliable zero-touch network system. DAEMON designs an end-to-end NI-native architecture for Beyond 5G (B5G) that fully coordinates NI-assisted functionalities.</p> <p>Main website: <a href="https://www.youtube.com/redirect?event=video_description&redir_token=QUFFLUhqblFJekN4dzdUcHYzQXlMd19rS0xjSDNleWVlZ3xBQ3Jtc0tsSmpkdWVUaXBSVDRHb2w5WDhGZnN3S05DM0FSRk9VS3RDc1hTT0l6MVhtd1pvWmZ2bEtRRUFxb0xkRmRQa1lVWVF0a1B2M1RwdXlmS2lIWEF0LWt4Q1ZIQndSNHNJcVJaSVNQODgtRThEaHh4TDBuaw&q=https%3A%2F%2Fh2020daemon.eu%2F&v=VNMVe8S-Ees">https://h2020daemon.eu/</a></p>
aurora: A Machine Learning GWAS Tool For Analyzing Microbial Habitat Adaptation
<p>This upload contains compressed raw data to rerun examples in the main text of <em>aurora </em>and the first version of<em> aurora </em>used in the paper.</p>
A Self-Adaptive Deep Learning Method for Automated Eye Laterality Detection Based on Color Fundus Photography
<p>The data for "A Self-Adaptive Deep Learning Method for Automated Eye Laterality Detection Based on Color Fundus Photography", including original image data and labels, DL model files, training logs and validation results.</p> <p><strong>Content</strong>:</p> <p>- <strong>Ex1_Preprocessing</strong> # the first experiment: preprocessing methods comparison</p> <p> - <strong>history</strong> # validation results of different metrics of models </p> <p> - <strong>log</strong> # training record of models. Read by TensorBoard</p> <p> - <strong>model</strong> # model file. Read by Keras</p> <p>- <strong>Ex2_Self-adaptive model</strong> # the second experiment: development of eye laterality detection model</p> <p> - <strong>data</strong> # serialized image data, including data and ground-truth label. Read by Python Pickle package.</p> <p> - <strong>Xy.h5</strong> # training data</p> <p> - <strong>test_Xy.h5</strong> # testing data</p> <p> - <strong>history</strong> # validation results of different metrics of models </p> <p> - <strong>log</strong> # training record of models. Read by TensorBoard</p> <p> - <strong>model</strong> # model file. Read by Keras</p> <p> - <strong>with_adaptive</strong> # with self-adaptive strategy</p> <p> - <strong>without_adaptive</strong> # without self-adaptive strategy</p> <p>For code review, please see: <a href="https://github.com/keepgallop/Eye-laterality-detection">https://github.com/keepgallop/Eye-laterality-detection</a></p> <p>For more information, please contact liuchi_email@foxmail.com</p> <p> </p> <p> </p> <p> </p>
MD simulations files for: Enhanced Sampling of Biomolecular Slow Conformational Transitions Using Adaptive Sampling and Machine Learning
<div>Colvar files and related python scripts of Ala2 and Ala10 simulations.</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.