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51 results for “multi-domain”

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OpenNeuro52/100

Multi-Domain Task Battery (MDTB)

Open the record for dataset details and reuse information.

openCC0Jan 2020View details →

zenodo40/100

Multi-domain and Explainable Prediction of Changes in Web Vocabularies (code & data)

This deposit contains supplementary code & data for the paper 'Multi-domain and Explainable Prediction of Changes in Web Vocabularies' (K-CAP 2021). Web vocabularies (WV) have become a fundamental tool for structuring Web data: over 10 million sites use structured data formats and ontologies to markup content. Maintaining these vocabularies and keeping up with their changes are manual tasks with very limited automated support, impacting both publishers and users. Existing work shows that machine learning can be used to reliably predict vocabulary changes, but on specific domains (e.g. biomedicine) and with limited explanations on the impact of changes (e.g. their type, frequency, etc.). In this paper, we describe a framework that uses various supervised learning models to learn and predict changes in versioned vocabularies, independent of their domain. Using well-established results in ontology evolution we extract domain-agnostic and human-interpretable features and explain their influence on change predictability. Applying our method on 139 WV from 9 different domains, we find that ontology structural and instance data, the number of versions, and the release frequency highly correlate with predictability of change. These results can pave the way towards integrating predictive models into knowledge engineering practices and methods.

opencc-by-4.0Nov 2021View details →

zenodo40/100

Multi-Domain Outlier Detection Dataset

The Multi-Domain Outlier Detection Dataset contains datasets for conducting outlier detection experiments for four different application domains: <ol> <li>Astrophysics - detecting anomalous observations in the Dark Energy Survey (DES) catalog (data type: feature vectors)</li> <li>Planetary science - selecting novel geologic targets for follow-up observation onboard the Mars Science Laboratory (MSL) rover (data type: grayscale images)</li> <li>Earth science: detecting anomalous samples in satellite time series corresponding to ground-truth observations of maize crops (data type: time series/feature vectors)</li> <li>Fashion-MNIST/MNIST: benchmark task to detect anomalous MNIST images among Fashion-MNIST images (data type: grayscale images)</li> </ol> Each dataset contains a "fit" dataset (used for fitting or training outlier detection models), a "score" dataset (used for scoring samples used to evaluate model performance, analogous to test set), and a label dataset (indicates whether samples in the score dataset are considered outliers or not in the domain of each dataset).  To read more about the datasets and how they are used for outlier detection, or to cite this dataset in your own work, please see the following citation: Kerner, H. R., Rebbapragada, U., Wagstaff, K. L., Lu, S., Dubayah, B., Huff, E., Lee, J., Raman, V., and Kulshrestha, S. (2022). Domain-agnostic Outlier Ranking Algorithms (DORA)-A Configurable Pipeline for Facilitating Outlier Detection in Scientific Datasets. Under review for Frontiers in Astronomy and Space Sciences.

opencc-by-4.0Jan 2022View details →

zenodo40/100

A Multi-domain Benchmark for Personalized Search Evaluation

We provide large-scale multi-domain benchmark datasets for Personalized Search. Further information can be found <a href="https://github.com/AmenRa/a-multi-domain-benchmark-for-personalized-search-evaluation">here</a>.

opencc-by-4.0Jun 2022View details →

zenodo40/100

Multi-domain evaluation of a latest generation combustion engine: focusing on sound quality perception

Dataset for conference paper "Multi-domain evaluation of a latest generation combustion engine: focusing on sound quality perception"

opencc-by-4.0May 2024View details →

zenodo40/100

The ECOLOPES Voxel Model: Multi-domain data integration for ontology-aided generative computational design of ecological building envelopes

The research portrayed in this article is part of the research project ‘ECOlogical building enveLOPES: a game-changing design approach for regenerative ecosystems’ funded by Horizon 2020 Future and Emerging Technologies. The overall research project focuses on developing a multi-domain data-driven computational design framework for the design of ecological building enclosures that addresses humans, plants, animals and microbiota. This article focuses on the development of a key component of the computational workflow in which initial designs are computationally initiated generated and analyzed, namely the ECOLOPES Voxel Model that contains and correlates multi-domain spatialised data for the design process, and its interactions with other components of the ontology-aided generative computational design process for ecological building envelopes. This repository contains all relevant data produced in this paper. Extended technical description is available in the Appendix A to the published paper, containing listing and description of individual voxel data layers. Data were exported from the RDB server (PostgreSQL) in text-based, future-proof format (csv).

opencc-by-4.0Nov 2024View details →

dryad40/100

Data from: The pathogenic E139D mutation stabilizes a non-canonical active state of the multi-domain phosphatase SHP2

Open the record for dataset details and reuse information.

publicJul 2025View details →

dryad40/100

Data from: Deep mutational scanning of the multi-domain phosphatase SHP2 reveals mechanisms of regulation and pathogenicity

Open the record for dataset details and reuse information.

publicMar 2025View details →

zenodo36/100

Estimating Lexical Complexity in Multi-Domain Settings for the Russian language

Open the record for dataset details and reuse information.

opencc-by-4.0Dec 2023View details →

zenodo36/100

His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models

This repository (and several sub-repositories) contains the data for the manuscript "His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models" The current repository contains the de-identified metadata of WSIs. Additionally, it contains Supplementary Data S1-S7. Due to the large size of WSIs, the archives have been divided into several parts to satisfy the size limit of Zenodo. HMU-C dataset: part a: <a href="../doi/10.5281/zenodo.12636965">https://zenodo.org/doi/10.5281/zenodo.12636965</a> part b: <a href="../doi/10.5281/zenodo.12705912">https://zenodo.org/doi/10.5281/zenodo.12705912</a> HMU-1st dataset: part a: <a href="../doi/10.5281/zenodo.12710399">https://zenodo.org/doi/10.5281/zenodo.12710399</a> part b: <a href="../doi/10.5281/zenodo.12723825">https://zenodo.org/doi/10.5281/zenodo.12723825</a> part c: <a href="../doi/10.5281/zenodo.12724507">https://zenodo.org/doi/10.5281/zenodo.12724507</a> part d: <a href="../doi/10.5281/zenodo.12726785">https://zenodo.org/doi/10.5281/zenodo.12726785</a> Please fully download all the parts and concatenate them before extraction. Supplementary Data S1-S7: <a href="https://zenodo.org/doi/10.5281/zenodo.16763510">https://zenodo.org/doi/10.5281/zenodo.16763510</a>

opencc-by-4.0Jul 2024View details →

zenodo36/100

Let's talk scalability: The current status of multi-domain thermal comfort models as support tools for the design of office buildings (Dataset v1.2.1)

THE PUBLICATION The data set provided is complementary to the thermal comfort review by Mamulova et al., 2023, titled "Let's talk scalability: The current status of multi-domain thermal comfort models as support tools for the design of office buildings": <a href="https://doi.org/10.1016/j.buildenv.2023.110502">Link to full publication</a>. The scoping review examines 77 multi-domain thermal comfort studies and initiates a discussion on model scalability; a model parameter which facilitates the understanding and prediction of thermal comfort conditions in real-world practice. THE DATA This database contains 27 scalability parameters per study which are used to analyse current research practices. For the results, please consult the review publication, as this database only contains raw data. For clarity, a legend of the scalability parameters is provided below. *** PLEASE NOTE *** This data set may be utilised, altered and/or expanded. However, you are kindly asked to cite this data set, the review publication (if applicable) and contact the corresponding author at eugenemamulova@gmail.com.  <table> <tbody> <tr> <td>Citation</td> <td>Citation number used in Mamulova et al.,"Multi-Domain Thermal Comfort Models for Office Buildings: Are Current Practices Scalable?", (2023)</td> <td>E.g. 1</td> </tr> <tr> <td>First Author</td> <td>Surname of the main author, for reference purposes only.</td> <td>E.g. Al-Atrash</td> </tr> <tr> <td>Publication</td> <td>Publication year</td> <td>E.g. 2020</td> </tr> <tr> <td>Dependent A</td> <td>List of variables used to measure thermal perception</td> <td>E.g. Neutral  temperature/ Thermal sensation</td> </tr> <tr> <td>Dependent B</td> <td>Scale used to measure each dependent variable</td> <td> </td> </tr> <tr> <td>Interaction A</td> <td>List of interaction effect(s) included in the explanatory/predictive model(s) </td> <td>E.g. Thermal and age/ Thermal and acoustical and personality</td> </tr> <tr> <td>Interaction B</td> <td>Is/are the effect(s) statistically significant?</td> <td>E.g. yes/ no/ (unknown)</td> </tr> <tr> <td>Crossed A</td> <td>List of crossed effect(s) included in the explanatory/predictive model(s) </td> <td>E.g. Acoustical/ Personality/ Age</td> </tr> <tr> <td>Crossed B</td> <td> </td> <td>*Note: Temperature is a main effect and is not included in the list</td> </tr> <tr> <td>Explanatory A</td> <td>Type of explanatory model</td> <td>E.g. Observation/ Statistical/ N/A</td> </tr> <tr> <td>Explanatory B</td> <td>Description of the explanatory model</td> <td>E.g. Asymptotic General Symmetry Test to check significance of difference in thermal perception between window conditions</td> </tr> <tr> <td>Predictive A</td> <td>Does the article include a predictive model?</td> <td>E.g. yes/ no</td> </tr> <tr> <td>Predictive B</td> <td>Type of predictive algorithm</td> <td>E.g. Logistic regression/ N/A</td> </tr> <tr> <td>Predictive C</td> <td>Description or formulation of the predictive model</td> <td>E.g. Probability of feeling too hot and probability of feeling too cold in relation to sound pressure level</td> </tr> <tr> <td>Performance</td> <td>Reported predictive performance</td> <td>E.g. Accuracy = 80%/ F-score = 0.8/ N/A</td> </tr> <tr> <td>Location</td> <td>City in which the measurements take place</td> <td>E.g. Paris</td> </tr> <tr> <td>Period</td> <td>Period over which the measurements take place</td> <td>E.g. Jan-Feb 2020</td> </tr> <tr> <td>Start time</td> <td>Time of day at which the measurements begin</td> <td>*Note: Time of day is not reported for most field studies. For this reason, time of day is only recorded for laboratory experiements.</td> </tr> <tr> <td>Study type</td> <td>Type of building and whether the experimental conditions are controlled by the experiment leader</td> <td>E.g. Field (controlled)/ Field (uncontrolled)/ Lab (controlled)/ Lab (uncontrolled)</td> </tr> <tr> <td>Building layout</td> <td>Building layout</td> <td>E.g. Laboratory office (LO)/ Laboratory neutral (LN)/ Field office (FO)</td> </tr> <tr> <td>Exposure</td> <td>Exposure of the participant, in minutes, to the experimental conditions, excluding preparation time</td> <td>*Note: Exposure is not reported for most field studies. For this reason, exposure is only recorded for laboratory experiements and is assumed to be longer than 60 minutes.</td> </tr> <tr> <td>Number of buildings/chambers</td> <td>Number of different locations used for conducting measurements</td> <td>E.g. 1</td> </tr> <tr> <td>Number of participants</td> <td>Number of individuals who take part in each experiment</td> <td>*Note: Outliers who are subsequently excluded from the modelling phase are not  included.</td> </tr> <tr> <td>Survey type</td> <td>Description of the type of survey used for subjective measurements</td> <td>E.g. Longitudinal questionnaire/ Transverse questionnaire/ N/A</td> </tr> <tr> <td>Survey content</td> <td>Are the contents of the survey provided in the article?</td> <td>E.g. Available/ unavailable</td> </tr> <tr> <td>Survey source</td> <td>Is/are the source(s) of the survey items mentioned in the article?</td> <td>E.g. Available/ unavailable</td> </tr> <tr> <td>Survey reliability</td> <td>Is the reliability of the survey items reported in the article?</td> <td>E.g. Available/ unavailable</td> </tr> <tr> <td>Survey duration</td> <td>Is the survey duration reported in the article?</td> <td>E.g. Available/ unavailable</td> </tr> <tr> <td>Context A</td> <td>Overview of the contextual information provided by the authors</td> <td>E.g. Room layout/ Room dimennsions</td> </tr> <tr> <td>Context B</td> <td>Qualitative/quantitative contextual information</td> <td>E.g. Figure containing room layout/ 3m x 3m x 5m</td> </tr> <tr> <td>Contextual variables A</td> <td>List of contextual variable(s) measured by the researchers (see Fig. A.)</td> <td>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</td> </tr> <tr> <td>Contextual variables B</td> <td>Range of values included in the experiment and their respective units.</td> <td>E.g. figure</td> </tr> <tr> <td>Social variables A</td> <td>List of social variable(s) measured by the researchers (see Fig. A.)</td> <td>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</td> </tr> <tr> <td>Social variables B</td> <td>Range of values included in the experiment and their respective units.</td> <td>E.g. [1,2,3,4,5]</td> </tr> <tr> <td>Personal variables A</td> <td>List of contextual variable(s) measured by the researchers (see Fig. A.)</td> <td>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</td> </tr> <tr> <td>Personal variables B</td> <td>Range of values included in the experiment and their respective units.</td> <td>E.g. [red, blue]</td> </tr> <tr> <td>Physical variables A</td> <td>List of physical variable(s) measured by the researchers (see Fig. A.)</td> <td>*Note: List of all variables mentioned in the article, including those that are not included in the explanatory/predictive models.</td> </tr> <tr> <td>Physical variables B</td> <td>Range of values included in the experiment and their respective units</td> <td>E.g. dB(A)</td> </tr> <tr> <td>Full-factorial</td> <td>Is/are the experiment(s) full-factorial?</td> <td>*Note: Uncontrolled field experiments are automatically labelled as fractional factorial.</td> </tr> <tr> <td>(Participant) Control</td> <td>Do participants have control over one or more experimental conditions?</td> <td>E.g. Yes/ No</td> </tr> <tr> <td>With/between subjects</td> <td>Are the experimental conditions shared between or within the participants?</td> <td>E.g. w/ b</td> </tr> <tr> <td>Fixed variables A</td> <td>List of variables reported as constant during the measurements</td> <td>E.g. Relative humidity/ Metabolic rate</td> </tr> <tr> <td>Fixed variables A</td> <td>(Range of) values and their respective units.</td> <td>E.g. 30-40%/ 1.2 met</td> </tr> <tr> <td>Summary</td> <td>Description of the research outome (outcome of the explanatory and/or predictive modelling)</td> <td>E.g. Lack of perceived control has a significant negative effect on neutral temperatures.</td> </tr> <tr> <td>Evaluation</td> <td>Are the participants invited to evaluate their experience once the experiment has been completed? </td> <td>E.g. Yes/ no</td> </tr> </tbody> </table> Note: The data in v1.1.0 has not yet been optimised for analytics.

openMay 2023View details →

ClinicalTrials.gov36/100

Preventing Cognitive Decline: The CITA GO-ON Multi-domain Intervention Study

ClinicalTrials.gov study NCT04840030. IPD Sharing: YES. Countries: 1. Publications: 1.

controlledIPD-YESFeb 2026View details →

ClinicalTrials.gov36/100

A Multi-domain Intervention for Healthy Aging

ClinicalTrials.gov study NCT06767410. IPD Sharing: YES. Countries: 1. Publications: 30.

controlledIPD-YESFeb 2026View details →

ClinicalTrials.gov36/100

Systematic Multi-domain Alzheimer's Risk Reduction Trial

ClinicalTrials.gov study NCT03683394. IPD Sharing: NO. Countries: 1. Publications: 46.

closedIPD-NOFeb 2026View details →

zenodo32/100

Multi-Domain Translation between Single-Cell Imaging and Sequencing Data using Autoencoders

This record contains raw data related to the article "Multi-Domain Translation between Single-Cell Imaging and Sequencing Data using Autoencoders".

opencc-by-4.0Nov 2020View details →

zenodo32/100

MENYO-20k: A Multi-domain English - Yorùbá Corpus for Machine Translation

MENYO-20k is a multi-domain parallel dataset with texts obtained from news articles, ted talks, movie transcripts, radio transcripts, science and technology texts, and other short articles curated from the web and professional translators. The dataset has 20,100 parallel sentences split into 10,070 training sentences, 3,397 development sentences, and 6,633 test sentences (3,419 multi-domain, 1,714 news domain, and 1,500 ted talks speech transcript domain) The dataset is open but for non-commercial use because some of the data sources like <a href="https://www.ted.com/about/our-organization/our-policies-terms/ted-talks-usage-policy">Ted talks</a> and <a href="https://www.jw.org/en/terms-of-use/#link0">JW news</a> requires permission for commercial use. Acknowledgement: This project was supported by the <a href="https://www.k4all.org/project/language-dataset-fellowship/">AI4D language dataset fellowship</a> through K4All and Zindi Africa

opencc-by-nc-4.0Nov 2020View details →

zenodo32/100

Multi-Domain Dataset for Robots (MDDRobots) - Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots

<h2>License</h2> The MDDRobots dataset is made available under the CC BY 4.0 license <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a>. <h2>Summary</h2> The Multi-Domain Dataset for Robots (MDDRobots) contains data for computer vision problems, indoor visual place recognition, and anomaly detection. The recorded images are from different cameras and indoor environmental conditions.  It is obligatory to cite the following paper in every work that uses the dataset: Wozniak, P., Krzeszowski, T. & Kwolek, B. Multi-Domain Indoor Dataset for Visual Place Recognition and Anomaly Detection by Mobile Robots. Sci Data 12, 817 (2025). https://doi.org/10.1038/s41597-025-05124-3 <h2>Data description</h2> The data are divided into five sets (containing data for different cameras), which have further subsets. Each of the subsets: Training, Test 1, Test 2, and Test 3 consists of nine image sequences. A total of 89,550 three-channel RGB color images in PNG format are organized into 20 zip folders with a whole size of 34.3 GB. Each image in the sequence has a label that represents a room. The number of images for each subset differs due to the split into training and testing data. The difference also results from different methods of recording the image sequences. In order to have balanced data in the subsets, each room in the sequence has the same number of images. Different environmental changes were introduced in each subset. The data from Test 1 are closest to those from the training set. The differences between the sequences are mainly due to changes in the route, robot, and recording equipment. The rooms are well lighted, but not overexposed. The sequences from Test 3 present changed conditions, such as a different time of day, a changed lighting system, and intensive layout changes. The key change is the different paths of the human and the robot. This means a different perspective from previously recorded scenes. The Test 2 sequences pose the most difficult challenge because they contain various recorded activities performed by people moving around rooms. People can occlude important parts of the scene and pass in front of the camera. The images were anonymized by manually blurring the faces of observed people. <h2>Dataset structure </h2> <ul> <li>RobotPiCamera_DataSet <ul> <li>DataSet_RobotPiCamera_RGB_train</li> <li>DataSet_RobotPiCamera_RGB_test1</li> <li>DataSet_RobotPiCamera_RGB_test2</li> <li>DataSet_RobotPiCamera_RGB_test3</li> </ul> </li> <li> Xtion_DataSet <ul> <li>DataSet_XTION_RGB_train</li> <li>DataSet_XTION_RGB_test1</li> <li>DataSet_XTION_RGB_test2</li> <li>DataSet_XTION_RGB_test3</li> </ul> </li> <li> GOPRO_DataSet <ul> <li>DataSet_GOPRO_RGB_train</li> <li>DataSet_GOPRO_RGB_test1</li> <li>DataSet_GOPRO_RGB_test2</li> <li>DataSet_GOPRO_RGB_test3</li> </ul> </li> <li>iPhone_DataSet <ul> <li>DataSet_IPHONE_RGB_train</li> <li>DataSet_IPHONE_RGB_test1</li> <li>DataSet_IPHONE_RGB_test2</li> <li>DataSet_IPHONE_RGB_test3</li> </ul> </li> <li>P40PRO_DataSet <ul> <li>DataSet_P40PRO_RGB_train</li> <li>DataSet_P40PRO_RGB_test1</li> <li>DataSet_P40PRO_RGB_test2</li> <li>DataSet_P40PRO_RGB_test3</li> </ul> </li> </ul> Example folder content: DataSet_P40PRO_RGB_train\Corridor1_RGB - 00000000.png, 00000001.png, 00000002.png, 00000003.png, ... 00000599.png. Total Images (Images per Place) <table> <tbody> <tr> <td>Subset</td> <td>Mounted</td> <td>Training</td> <td>Test 1</td> <td>Test 2</td> <td>Test 3</td> </tr> <tr> <td>Pi Camera</td> <td>Robot</td> <td>7200 (800)</td> <td>5400 (600)</td> <td>5400 (600)</td> <td>5400 (600)</td> </tr> <tr> <td>Xtion</td> <td>Robot</td> <td>7200 (800) </td> <td>1800 (200) </td> <td>1800 (200)</td> <td>1800 (200) </td> </tr> <tr> <td>GoPro</td> <td>Hand</td> <td>5400 (600)</td> <td>4500 (500)</td> <td>4500 (500)</td> <td>4500 (500)</td> </tr> <tr> <td>iPhone</td> <td>Hand</td> <td>5400 (600) </td> <td>4500 (500)</td> <td>4500 (500)</td> <td>4500 (500) </td> </tr> <tr> <td>P40Pro</td> <td>Hand</td> <td>5400 (600)</td> <td>4050 (450)</td> <td>3150 (350) </td> <td>3150 (350) </td> </tr> </tbody> </table> <h2> Further information</h2> For any questions, comments or other issues please contact Piotr Woźniak <p.wozniak@prz.edu.pl>.

opencc-by-4.0Aug 2024View details →

zenodo32/100

Processed KuaiRand-1K dataset for the paper: Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized Integration Framework

The original public dataset is published in https://zenodo.org/records/10439422, we processed the KuaiRand-1K dataset for the paper: Large-Scale Multi-Domain Recommendation: an Automatic Domain Feature Extraction and Personalized  Integration Framework.

opencc-by-4.0Apr 2024View details →

zenodo32/100

His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models

partab of His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models

opencc-by-4.0Jul 2024View details →

zenodo32/100

Input Files and Processed Results for FreeMHD: validation and verification of the open-source, multi-domain, multi-phase solver for electrically conductive flows

Input Files (StartingFiles.zip) and Processed Results (FreeMHDPaperAllFigures.zip) used to make paper figures.    FreeMHD: validation and verification of the open-source, multi-domain, multi-phase solver for electrically conductive flows The extreme heat fluxes in the divertor region of tokamaks may require an alternative to solid plasma-facing components, for the extraction of heat and the protection of the surrounding walls. Flowing liquid metals are proposed as an alternative, but raise additional challenges that require investigation and numerical simulations. Free surface designs are desirable for plasma-facing components (PFCs), but steady flow profiles and surface stability must be ensured to limit undesirable interactions with the plasma. Previous studies have mainly used steady-state, 2D, or simplified models for internal flows and have not been able to adequately model free-surface liquid metal (LM) experiments. Therefore, FreeMHD has been recently developed as an open-source magnetohydrodynamics (MHD) solver for free-surface electrically conductive flows subject to a strong external magnetic field. The FreeMHD solver computes incompressible free-surface flows with multi-region coupling for the investigation of MHD phenomena involving fluid and solid domains. The model utilizes the finite-volume OpenFOAM framework under the low magnetic Reynolds number approximation. FreeMHD is validated using analytical solutions for the velocity profiles of closed channel flows with various Hartmann numbers and wall conductance ratios. Next, experimental measurements are then used to verify FreeMHD, through a series of cases involving dam breaking, 3D magnetic fields, and free-surface LM flows. These results demonstrate that FreeMHD is a reliable tool for the design of LM systems under free surface conditions at the reactor scale. Furthermore, it is flexible, computationally inexpensive, and can be used to solve fully 3D transient MHD flows.

opencc-by-4.0Oct 2024View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas

neuroscienceopenDocumentation, web resources, and API references are available online.

Last verified 2026-04-30Open record

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.

abode-home-cage

behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.

Last verified 2026-04-30Open record

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.

dandi-nwb

electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.

Last verified 2026-04-30Open record

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.

ibl

behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.

Last verified 2026-04-29Open record

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.

openneuro

neuroscienceopenPublished datasets are available on demand over the internet.

Last verified 2026-04-29Open record