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.
116
datasets available to search
ShareScore release 0.9.0
Dataset results
116 results for “physical systems”
Data of the article Analysis of the self-archiving policies of journals in the highest rank category of the Finnish journal classification system within computer science, physics and electronic engineering
<p>The publication forum level three journals representing the three fields of science of computer science, computer science and electrical engineering were identified by utilizing the MinEdu field search filter while searching for the top-ranked journals from the publication channel search (https://www.tsv.fi/julkaisufoorumi/haku.php?lang=en), which is based on Field of Science, Statistics Finland classification (https://www.stat.fi/meta/luokitukset/tieteenala/001-2010/index_en.html). The data were extracted during august 2017 consists of total of 127 individual journals. It is worth noting that circa 30 journals were classified into more than one fields of sciences under scrutiny. First, the journals were divided into representing gold and hybrid model journals. Second, green open access policies of the identified hybrid journals were analyzed using Laakso’s (2014) publisher policy coding framework. Also publishers of the individual journals were identified and subsequently added to the data.</p> <p>NOTE! The data includes the shortest embargo to either institutional or subject repositories. For example, Elsevier had no embargo to opening accepted manuscripts from arXiv subject repository and thus no embargoes to Elsevier's journals are included within this datasheet.</p> <p>Data is in CSV. format</p> <p> </p> <p> </p>
Dataset of the scientific paper " Multimodal robotic system for upper-limb rehabilitation in physical environment" (Advances in Mechanical Engineering)
<p>There are eight files with the following information:<br> - pos_stateXX.bin, binary file with information of the end effector position of the robot device in meters along the three axis (X, Y, Z) during state XX of the experiment<br> - target_stateXX.bin, binary file with information of the target position for the robot device in meters along the three axis (X, Y, Z) during state XX of the experiment<br> - emg_channelXX.bin, binary file with information of channel 1 of the EMG sensor in mV during during the whole time of the experiment<br> - color_stateXX.bin, binary file with information of color filter information during state XX of the experiment. This information is the percentage of pixels with the correct color (yellow, cyan or magenta) inside the region of interest</p> <p> </p>
Datasets of synthetic workflows for cyber-physical edge-hub-cloud systems
<p>These datasets of synthetic workflows were generated to evaluate the performance and scalability of a multi-constrained scheduling approach for workflow applications of various structures, sizes, and sensing/actuating requirements in a cyber-physical system (CPS) following the edge-hub-cloud paradigm. The examined CPS comprised four edge devices (i.e., single-board computers, each attached to an unmanned aerial vehicle (UAV) equipped with sensors/actuators) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. All system devices featured heterogeneous multicore processors and varied sensing/actuating or other specialized capabilities. The problem objective was the minimization of the overall latency of the application under deadline, memory, storage, energy, capability, and task precedence constraints.</p> <p>We generated 25 random workflows (task graphs) with 10, 20, 30, 40, and 50 nodes (5 task graphs for each size), utilizing the Task Graphs For Free (TGFF) random task graph generator [1],[2]. Additional task parameters (e.g., execution time, power consumption, memory, storage, output data size, capability) were included post-generation, using appropriate values. More details are provided in README.txt and in [3].<br><br>References:<br>[1] R. P. Dick, D. L. Rhodes, and W. Wolf, "TGFF: Task graphs for free," in Proc. Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE), 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.</p> <p>[2] R. P. Dick, D. L. Rhodes, and K. Vallerio, "TGFF," https://robertdick.org/projects/tgff/.</p> <p>[3] A. Kouloumpris, G. L. Stavrinides, M. K. Michael, and T. Theocharides, “Optimal multi-constrained workflow scheduling for cyber-physical systems in the edge-cloud continuum,” in Proc. 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), Jul. 2024, pp. 483-492, doi: 10.1109/COMPSAC61105.2024.00072.</p>
Morphological and physical chemical characterization of main agricultural plastics articles used for protected cultivation systems during ageing in fields, and collection practices
<p>This dataset includes data generated upon the implementation of the ST 1.2.1 "Analysis of degradation and fragmentation of AP and transfer of MNP to soil". The activities dealt with the study of degradation and fragmentation from weathering and agricultural practices of conventional and biodegradable AP relevant for transfer of MNP to soil (during both use and end of life). In particular, the experimental data refer to characterization of biodegradable mulch films, pristine (coded M-BIO0) or subjected to photo-oxidative weathering (M-BIO192), as well as the same samples buried in soil for varying time periods, up to 353 days. The folders included contain gel permeation chromatography (GPC) and Matrix-assisted Laser Desorption Ionization (MALDI-TOF) data, which account for the change in film molecular weight upon soil burial. Furthermore, Differential Scanning Calorimetry (DSC) data and Scanning Electron Microscopy (SEM) and Water Contact Angle (WCA) images of some selected samples are also provided. The folder named MS RAW FILES.zip includes all the mass spectrometry raw data.</p>
Transformers for Modeling Physical Systems
<p>Data set associated with the publication <a href="https://arxiv.org/abs/2010.03957">Transformers for Modeling Physical Systems</a>. Transformers are widely used in natural language processing due to their ability to model longer-term dependencies in text. Although these models achieve state-of-the-art performance for many language related tasks, their applicability outside of the natural language processing field has been minimal. In this work, we propose the use of transformer models for the prediction of dynamical systems representative of physical phenomena. </p> <p>This data set includes data in HDF5 files for:</p> <p>Lorenz ODE:</p> <ul> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/lorenz_training_rk.tar.gz?versionId=c4bd1230-3b22-4d2e-83f0-357146a90423">lorenz_training_rk.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/lorenz_valid_rk.tar.gz?versionId=3cf95dac-a75d-42d8-85ab-615f7a2ad67f">lorenz_valid_rk.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/lorenz_test_rk.tar.gz?versionId=bbb4bd3d-33c9-4903-98ed-f7a926dc95db">lorenz_test_rk.tar.gz</a></li> </ul> <p>Flow Around a Cylinder:</p> <ul> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/cylinder_training.tar.gz?versionId=25bd1f3a-03b7-44d0-aa3f-afaffa6cd706">cylinder_training.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/cylinder_valid.tar.gz?versionId=58a6e98d-be41-4603-91cb-9522d848cf57">cylinder_valid.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/cylinder_test.tar.gz?versionId=a3c03293-0278-40ee-b181-2eb53a8d0b47">cylinder_test.tar.gz</a></li> </ul> <p>Gray-Scott Reaction-Diffusion:</p> <ul> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/grayscott_training.tar.gz">grayscott_training.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/grayscott_valid.tar.gz?versionId=3bb8aa25-c9c8-494e-a206-e8d1fdb8a88f">grayscott_valid.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/grayscott_test.tar.gz?versionId=d9cee8a6-b22f-44b9-ae1b-2433caf77e34">grayscott_test.tar.gz</a></li> </ul> <p>Rossler ODE:</p> <ul> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/rossler_training.tar.gz?versionId=d44be9cf-8fa7-4eb2-8b6e-8ac129822f51">rossler_training.tar.gz</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/rossler_valid.tar.gz?versionId=93c658e3-235a-4150-b233-ed514d6c7467">rossler_valid.tar.gz</a></li> </ul> <p>As well as several pretrained embedding models for the Google Collab notebooks on <a href="https://github.com/zabaras/transformer-physx/">Github</a>:</p> <ul> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/embedding_lorenz_pretrained.pth?versionId=cd30ff4e-34b3-4070-b346-718fb8526dac">embedding_lorenz_pretrained.pth</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/embedding_cylinder_pretrained.pth?versionId=69552efb-09e4-49d9-a224-ccc7cff91b86">embedding_cylinder_pretrained.pth</a></li> <li><a href="https://zenodo.org/api/files/8580a888-775c-471a-95ef-5e6df6f085a2/embedding_rossler_pretrained.pth?versionId=e7d1f4aa-3f31-45fe-91e4-8411e9cd634f">embedding_rossler_pretrained.pth</a></li> </ul> <p>See the Github repository for code base: <a href="https://github.com/zabaras/transformer-physx/">https://github.com/zabaras/transformer-physx/</a></p>
Advanced open source data formats for geometrically and physically coupled systems - examples
<p>Some model files to describe a geometrically and physically coupled PDE and a ODE system, arising from discretization in space. The files correspond to:</p> <ul> <li>a simplified two component problem with coupling and an FMU in <strong>withFMU.json</strong></li> <li>the corresponding ODE in <strong>io_withFMU_FECoupled.json</strong></li> <li>a PDE model of a complete machine in <strong>ictimt_coupledModel.zip</strong>. This model belongs to https://doi.org/10.17973/MMSJ.2021_7_2021072</li> <li>the corresponding discrete model in <strong>ictimt_feCoupled.zip </strong>This model belongs to https://doi.org/10.17973/MMSJ.2021_7_2021072</li> </ul> <p> </p> <p> </p>
Design Methodologies and Engineering Applications for Ecosystem Biomimicry: An Interdisciplinary Review Spanning Cyber, Physical, and Cyber-Physical Systems
<p>This is the data for the interdisciplinary review on ecosystem biomimicry for engineering applications. </p>
Measurements from CalCOFI cruises in the California Current System, including log of station information, weather, sea conditions as well as physical, chemical and biological measurements including including temperature, salinity, oxygen, density, sigma theta, phosphate, silicate, nitrite, nitrate, ammonia, chlorophyll a, integrated chlorophyll a, primary productivity, and integrated primary production. 1949 - January 2020
Since 1949, hydrographic and biological data of the California Current System have been collected on quarterly CalCOFI cruises. The 59+ year hydrographic time-series includes weather, temperature, salinity, oxygen and phosphate observations. In 1961, nutrient analysis expanded to include silicate, nitrate and nitrite; in 1973, chlorophyll was added; in 1984, C14 primary productivity incubations were added. These data are being provided here in collaboration with CalCOFI-SIO in order to provide an additional queriable interface to the data. The data are updated on a regular basis from the CalCOFI hydrographic database.
Dataset of Optimization Methods for Model-Implemented Fault Injection in Cyber-Physical Systems: A Systematic Literature Review
<p>Data set for the paper entitled “<strong>Optimization Methods for Model-Implemented Fault Injection in Cyber-Physical Systems: a Systematic Literature Review</strong>”</p> <p>In this repo, we have some pictures and Excel files.</p> <ul> <li>Pictures are screenshots from the Parsifal tool (https://parsif.al/) which we use for performing the SLR.</li> <li>Excel files are as follows:</li> </ul> <table style="border-collapse: collapse; width: 100%;"><colgroup><col style="width: 21.8789%;"><col style="width: 78.1211%;"></colgroup> <tbody> <tr> <td><strong>Excel’s file name</strong></td> <td><strong>Description</strong></td> </tr> <tr> <td>Keyword_analysis </td> <td>In this file, you can see the evolution of our keyword selection.</td> </tr> <tr> <td>Articles_InclusionExclusion_QA </td> <td>In this file, you can find all found papers until Feb. 27, 2025. In the last column of this excel file, we can see the status of each paper, if it has been included, or excluded by authors. For the included paper (their status is “Accepted”) you can see their quality score in the last column.</td> </tr> <tr> <td>Extracted_data </td> <td>In this file, we logged the result of data extraction from qualified paper. In the first sheet “Articles”, you can see a list of the read papers with corresponding data. Other sheets in this Excel file are driven from the “Article” sheet for data visualization. So, they are not important.</td> </tr> </tbody> </table> <p> <br>If you have any questions, you can read the corresponding paper and contact the authors.</p>
Physical and technical characterization of main agricultural plastics articles used for protected cultivation systems
<p>The folder "Thermal and spectroscopic data on the characterization of mulch films.rar" includes differential scanning calorimetry (DSC), thermogravimetry (TG), Fourier-transform infrared spectroscopy (FTIR), and water contact angle (WCA) raw data and images relevant to the characterization of conventional and biodegradable mulch films. 7 mulching films consisting of 3 different conventional linear low-density polyethylene (LLDPE, coded as PE) films and 4 different biodegradable (PBAT-based, coded as BIO) films are analysed.In particular, data regarding M-BIOIT-15-black-0, M-PEIT-15-black-0, M-BIOEL-15-black-0, M-PEEL-15-black-0, M-PEEL-50-transparent-0, M-BIODE-30-black-0 and M-BIOFI-15-black-0 are included.</p>
Cyber-Physical System power Consumption
<h1>Files</h1> <p>This dataset is comprised of 5 CSV files contained in the data.zip archive. Each one represents a production machine from which various sensor data has been collected. The average cadence for collection was 5 measurements per second. The monitored devices where used for hydroforming.</p> <p>The collection period covered the period from 2023-06-01 until 2023-08-05.</p> <h2>Data</h2> <p>These files represent a complete data dump from the data available in the time-series database, InfluxDB, used for collection. Because of this some columns have no semantic value for detecting production cycles or any other analytics.</p> <p>Each file contains a total of 14 columns. Some of the columns are artefacts of the query used to extract the data from InfluxDB and can be discarded. These columns are: results, table _start, _stop</p> <ul> <li><em>results</em> - An artefact of the InfluxDB query, signifies postprocessing of results in this dataset. It is "mean".</li> <li><em>table</em> - An artefact of the InfluxDB query, can be discarded.</li> <li><em>_start</em> and <em>_stop</em> - Refers to ingestion related data, used in monitoring ingestion. </li> <li><em>_field</em> - An artefact of the InfluxDB query, specifying what field to use for the query.</li> <li><em>_measurement</em> - An artefact of the InfluxDB query, specifying what measurement to use for the query. Contains the same information as device_id.</li> <li><em>host</em> - An artefact of the InfluxDB query, the unique name of the host used for the InfluxDB sink in Kubernetes.</li> <li><em>kafka_topic</em> - Name of the Kafka topic used for collection.</li> </ul> <p> </p> <p>Pertinent columns are:</p> <ul> <li><strong><em>_time</em></strong> - Denotes the time at which a particular event has been measured, it is used as index when creating a dataframe.</li> <li><em><strong>_time.1</strong></em> - Duplicate of _time for sanity check and ease of analysis when _time is set as index</li> <li><em><strong>_value</strong></em> - Represents the value measured by each sensor type.</li> <li><em><strong>device_id </strong></em>- Unique identifier of the manufacturing device, should be the same as the file name, i.e. B827EB8D8E0C.</li> <li><em><strong>ingestion_time</strong></em> - Timestamp when the data has been collected and ingested by influxDB.</li> <li><em><strong>sid</strong></em> - Unique sensor ID; the power measurements can be found at sid 1.</li> </ul> <p> </p> <h1>Annotations</h1> <p>There are two additional files which contain annotation data: </p> <ul> <li><em><strong>scamp_devices.csv</strong></em> - Contains mapping information between the dataset device ID (defined in column "<em>DeviceIDMonitoring</em>") and the ground truth file ID (defined in column "<em>DeviceID</em>")</li> <li><em><strong>scamp_report_3m.csv </strong></em>- Contains the ground truth, which can be used for validation of cycle detection and analysis methods. The columns are as follows: <ul> <li><strong><em>ReportID</em></strong> - Internal unique ID created during data collection. It can be discarded.</li> <li><em><strong>JobID</strong></em> - Internal Scheduling Job unique ID.</li> <li><em><strong>DeviceID</strong></em> - The unique ID of the devices used for manufacturing needs to be mapped using the <em>scamp_device.csv</em> data.</li> <li><em><strong>StartTime</strong></em> - Start time of operations</li> <li><em><strong>EndTime</strong></em> - End time of operations</li> <li><em><strong>ProductID</strong></em> - Unique identifier of the product being manufactured.</li> <li><em><strong>CycleTime</strong></em> - Average length of cycle in seconds, added manually by operators. It can be unreliable.</li> <li><em><strong>QuantityProduced</strong></em> - Number of products manufactured during the timeframe given by <em>StartTime</em> and <em>EndTime</em>.</li> <li><em><strong>QuantityScrap</strong></em> - Number of scraped/malformed products in the given timeframe. These are part of the <em>QuantityProduced</em><em>,</em><strong> </strong>not in addition to it.</li> <li><em><strong>IntreruptionMinuted</strong></em> - Minutes of production halt.</li> </ul> </li> <li><em><strong>scamp_patterns.csv</strong></em> - Contains the start and end timestamp for selected example production cycles. These where chosen based on expert users.</li> </ul> <h1>Jupyter Notebook</h1> <p>We have provided a sample Jupyter notebook (<em>verify_data.ipynb</em>), which gives examples of how the dataset can be loaded and visualised as well as examples of how the sample patterns and ground truth can be addressed and visualised.</p> <h2>Note</h2> <p>The Jupyter Notebook contains an example of how the data can be loaded and visualised. Please note that both data should be filtered based on sid; the power measurements are collected by sid 1. See Notebook for example.</p>
Dataset and scripts for manuscript "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models"
<p>Please note: The title of this version contains an updated title for the manuscript compared to the previous version of this dataset. This is only due to title updates during the peer review process for the manuscript.</p> <p>The zip file contains the scripts, functions, and source files for the manuscript titled "Using Neural Network Ensembles to Separate Ocean Biogeochemical and Physical Drivers of Phytoplankton Biogeography in Earth System Models." The manuscript has been submitted for peer review.</p> <p>Please consult the README file for information on the specifications of the files.</p> <p>These files may occasionally be updated to add annotations to the scripts to make them more user friendly and to correct any errors.</p>
Dataset of the manuscript "Problems and Solutions in Applying Continuous Integration and Delivery to 20 Open-Source Cyber-Physical Systems"
<p>This archive contains the artifacts (datasets) for the manuscript "Problems and Solutions in Applying Continuous Integration and Delivery to 20 Open-Source Cyber-Physical Systems"</p>
Dataset of the paper "An Empirical Characterization of Software Bugs in Open-Source Cyber-Physical Systems"
<p><br> #Dataset Package for the paper "An Empirical Characterization of Software Bugs in Open-Source Cyber-Physical Systems"</p> <p><br> Description of the content:</p> <p><br> 1) "1_RQ-CPS-bugs-Taxonomy" folder contains all the main experimental data concerning the issues sampled and analyzed from all the Projects considered in the study,<br> including row-data on the taxonomy validtion steps.<br> <br> - Under "the sub-folder "1_Taxonomy-Raw-data" are reported the row-data concerning the taxonomy validtion steps </p> <p><br> 2) "2_Scripts" contains all scripts used to generate the issue data and sampled issue raw-data in the previous folders: </p> <p><br> - "setup.md" file in the folder describes how to set=up and run the script used for collecting and sampling the issues for the validation steps:<br> <br> - runJSONtoCSV.sh<br> - JSONtoCSV.py<br> - generateListOfAllSamples.py<br> - generateAllSamples.r<br> <br> Under "the sub-folder "1_Scripts/1_Data_Collection":<br> <br> <br> 3) "3_Final Taxonomy" folder contains the final Table representation (also reported in the previous folder) and main figures of the CPSs Bugs Taxonomy.</p>
Continuous Integration and Delivery Practices for Cyber-Physical Systems: An Interview-Based Study - Online Dataset
<p>This package contains the online dataset of the manuscript:</p> <p>Continuous Integration and Delivery Practices for Cyber-Physical Systems: An Interview-Based Study</p>
Figure 4: Nyquist diagrams ² 00 (!¿¾) = f(² 0 (!¿¾))T 166-TOWARD THE PHYSICAL BASIS OF COMPLEX SYSTEMS: DIELECTRIC ANALYSIS OF POROUS SILICON NANOCHANNELS IN THE ELECTRICAL DOUBLE LAYER LENGTH RANGE
<p>Fig.4. This behaviour denotes that the EDL is not an ideally<br> capacitor, but also is not a disipative region, depending both on the EDL<br> thickness and the frequency range of the applied ¯eld [7]. The composition<br> (by thickness) of the EDL determines essentially the dielectric response of the<br> interface system. Compared with experimental results, the dielectric pro¯le<br> of this higher length scales model, can provides a more complet description of<br> the solvent properties for a given electrode.</p>
Figure 1: EDL structure for p-Si-TOWARD THE PHYSICAL BASIS OF COMPLEX SYSTEMS: DIELECTRIC ANALYSIS OF POROUS SILICON NANOCHANNELS IN THE ELECTRICAL DOUBLE LAYER LENGTH RANGE
<p>Figure 1: EDL structure for p-Si (SCL negative charged,²F < ²FR )/aqueous<br> solvent interface. The electrostatic potential and charged atoms in solvent<br> distributions vs the distance z from the wall.</p>
Figure 3: The dependencies ² 00 (log !¿¾). The values are normalized at ² 00 max-TOWARD THE PHYSICAL BASIS OF COMPLEX SYSTEMS: DIELECTRIC ANALYSIS OF POROUS SILICON NANOCHANNELS IN THE ELECTRICAL DOUBLE LAYER LENGTH RANGE
<p>Fig.3. The conductivity relaxation occurs at<br> lowing frequencies. The form of the ²<br> 00<br> (!) = f(²<br> 0<br> (!)) diagrams changes from<br> a vertical line (a), to any deformate semicircles (b, c, d) having the angle to<br> real axe below ¼<br> 2 , Fig.4. This behaviour denotes that the EDL is not an ideally<br> capacitor, but also is not a disipative region, depending both on the EDL<br> thickness and the frequency range of the applied ¯eld [7]. The composition<br> (by thickness) of the EDL determines essentially the dielectric response of the<br> interface system. Compared with experimental results, the dielectric pro¯le<br> of this higher length scales model, can provides a more complet description of<br> the solvent properties for a given electrode.</p>
Figure 2: The dependencies ² 0 (log !¿¾). The values are normalized at ² 0 max-TOWARD THE PHYSICAL BASIS OF COMPLEX SYSTEMS: DIELECTRIC ANALYSIS OF POROUS SILICON NANOCHANNELS IN THE ELECTRICAL DOUBLE LAYER LENGTH RANGE
<p>The results of the model are shown that the frequency-dependences ²<br> 0<br> (log(!¿¾))<br> in Fig.2, ²<br> 00(log(!¿¾)) in Fig.3 and ²<br> 00<br> (²<br> 0<br> )T in Fig.4, where ²<br> 0<br> , ²<br> 00<br> are the real and<br> imaginary part, respectively, from (7), having the ¸D<br> ¸ ratio as parameter.</p>
BRAIN Journal-Isomorphism Between Estes' Stimulus Fluctuation Model and a Physical- Chemical System-Figure 1. Two compartments containing solution separated by a membrane.
<p>In fact, this equation will be found first if one consults physical or chemical textbooks for<br> diffusion. Also one may be able to find already-existing diffusion simulators to see vivid images of<br> the process.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.