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51
datasets available to search
ShareScore release 0.7.1
Dataset results
51 results for “multi-domain”
A Multi-domain Lifestyle Intervention Among Aged Community-residents in Zhejiang, China
ClinicalTrials.gov study NCT05886114. IPD Sharing: NO. Countries: 1. Publications: 2.
A Group Study on the Effects of a Short Multi-Domain Cognitive Training in Healthy Elderly Italian People
ClinicalTrials.gov study NCT03771131. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
The Influence of Multi-domain Cognitive Training on Large-scale Structural and Functional Brain Networks in MCI
ClinicalTrials.gov study NCT03883308. IPD Sharing: Not stated. Countries: 1. Publications: 1.
A Multi-domain and Multi-component Falls Intervention Program for Community- Dwelling Older Adults: SAFE-TECH
ClinicalTrials.gov study NCT06102954. IPD Sharing: NO. Countries: 1. Publications: 3.
Omega-3 Fatty Acids and/or Multi-domain Intervention in the Prevention of Age-related Cognitive Decline
ClinicalTrials.gov study NCT00672685. IPD Sharing: Not stated. Countries: 2. Publications: 23.
Impact of the Digital Multi-domain Cognitive Intervention in High-risk Populations for Dementia
ClinicalTrials.gov study NCT06442943. IPD Sharing: YES. Countries: 1. Publications: 1.
Multi-domain Online Therapeutic Investigation Of Neurocognition (MOTION)
ClinicalTrials.gov study NCT05217849. IPD Sharing: NO. Countries: 1. Publications: 1.
Multi-domain Versus Uni-Domain Training on Executive Control and Memory Functions of Older Adults
ClinicalTrials.gov study NCT03823183. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Impacts and Testing of the "Multi-domains Active-living Program" in Operable Non-Muscle Invasive Bladder Cancer Patients
ClinicalTrials.gov study NCT05739968. IPD Sharing: NO. Countries: 1. Publications: 22.
Evaluation of Pilot Community-based Multi-domain Program Older Adults at Risk of Cognitive Impairment
ClinicalTrials.gov study NCT04440969. IPD Sharing: NO. Countries: 1. Publications: 8.
Multi-Domain Exercise and Memory in Adults Relative to ApoE Genotype: A fMRI Study
ClinicalTrials.gov study NCT05068271. IPD Sharing: NO. Countries: 1. Publications: 1.
Multi-domain Distribution Learning for De Novo Drug Design
<p>Model checkpoints, processed dataset and samples.</p>
His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models
Open the record for dataset details and reuse information.
His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models
Open the record for dataset details and reuse information.
His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models
Open the record for dataset details and reuse information.
His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models
<p>part aa of HMU1st</p>
ABILITY - TelerehABILITation: TechnologY-enhanced Multi-domain at Home Continuum of Care Program
ClinicalTrials.gov study NCT02746484. IPD Sharing: Not stated. Countries: 0. Publications: 2.
Psychological and Lifestyle Factors That Predict Adherence of Multi-domain Interventions for Promoting Brain Health
ClinicalTrials.gov study NCT07387523. IPD Sharing: NO. Countries: 1. Publications: 0.
MedIMeta: A comprehensive and easy-to-use multi-domain multi-task medical imaging meta-dataset
<p>We introduce the Medical Imaging Meta-Dataset (MedIMeta), a novel multi-domain, multi-task meta-dataset designed to facilitate the development and standardised evaluation of ML models and cross-domain few-shot learning algorithms for medical image classification. MedIMeta contains 19 medical imaging datasets spanning 10 different domains and encompassing 54 distinct medical tasks, offering opportunities for both single-task and multi-task training. All tasks are standardised to the same format and readily usable in PyTorch or other ML frameworks. All datasets have been previously published with an open license that allows redistribution or we obtained an explicit permission to do so.</p> <p>Each dataset within the MedIMeta dataset is standardized to a size of 224 × 224 pixels which matches image size commonly used in pre-trained models. Furthermore, the dataset comes with pre-made splits to ensure ease of use and standardized benchmarking. We release a user-friendly Python package to directly load images for use in PyTorch.<br><br></p> <h3>Links</h3> <ul> <li>Project website: <a href="https://www.woerner.eu/projects/medimeta/" target="_blank" rel="noopener">https://www.woerner.eu/projects/medimeta/</a></li> <li>Data loading code (medimeta Python package): <a href="https://github.com/StefanoWoerner/medimeta-pytorch" target="_blank" rel="noopener">https://github.com/StefanoWoerner/medimeta-pytorch</a></li> <li>Data creation code: <a href="https://github.com/StefanoWoerner/medimeta-dataset-scripts" target="_blank" rel="noopener">https://github.com/StefanoWoerner/medimeta-dataset-scripts</a></li> </ul> <p> </p> <h3>Dataset Overview</h3> <table> <tbody> <tr> <td><strong>Dataset Name</strong></td> <td><strong>Dataset ID</strong></td> <td><strong>License</strong></td> <td><strong>Domain</strong></td> <td><strong>Task Names</strong></td> <td><strong>Task Targets</strong></td> <td><strong># Labels</strong></td> </tr> <tr> <td>AML Cytomorphology</td> <td>aml</td> <td>CC BY-SA 4.0</td> <td>Microscopy</td> <td>morphological class</td> <td>multi-class classification</td> <td>15</td> </tr> <tr> <td>Breast Ultrasound</td> <td>bus</td> <td>CC BY-SA 4.0</td> <td>Breast ultrasound</td> <td>case category<br>malignancy</td> <td>multi-class classification<br>binary classification</td> <td>3<br>2</td> </tr> <tr> <td>Colorectal Cancer Histopathology</td> <td>crc</td> <td>CC BY-SA 4.0</td> <td>Histopathology</td> <td>tissue class</td> <td>multi-class classification</td> <td>9</td> </tr> <tr> <td>Chest X-ray Multi-disease</td> <td>cxr</td> <td>CC BY-SA 4.0</td> <td>Chest X-ray</td> <td>disease labels<br>patient sex</td> <td>multi-label classification<br>binary classification</td> <td>14<br>2</td> </tr> <tr> <td>Dermatoscopy</td> <td>derm</td> <td>CC BY-SA 4.0</td> <td>Dermatoscopy</td> <td>disease category</td> <td>multi-class classification</td> <td>7</td> </tr> <tr> <td>Diabetic Retinopathy (Regular Fundus)</td> <td>dr_regular</td> <td>CC BY-SA 4.0</td> <td>Retinal fundus</td> <td>DR level<br>Overall quality<br>Artifact<br>Clarity<br>Field definition</td> <td>ordinal regression<br>binary classification<br>ordinal regression<br>ordinal regression<br>ordinal regression</td> <td>5<br>2<br>6<br>5<br>5</td> </tr> <tr> <td>Diabetic Retinopathy (Ultra-widefield Fundus)</td> <td>dr_uwf</td> <td>CC BY-SA 4.0</td> <td>Retinal fundus</td> <td>DR level</td> <td>ordinal regression</td> <td>5</td> </tr> <tr> <td>Fundus Multi-disease</td> <td>fundus</td> <td>CC BY-SA 4.0</td> <td>Retinal fundus</td> <td>disease presence<br>disease labels</td> <td>binary classification<br>multi-label classification</td> <td>2<br>45</td> </tr> <tr> <td>Glaucoma-specific fundus images</td> <td>glaucoma</td> <td>CC BY-SA 4.0</td> <td>Retinal fundus</td> <td>Glaucoma suspect</td> <td>binary classification</td> <td>2</td> </tr> <tr> <td>Mammography (Calcifications)</td> <td>mammo_calc</td> <td>CC BY-SA 4.0</td> <td>Mammography</td> <td>pathology<br>calc type<br>calc distribution</td> <td>binary classification<br>multi-label classification<br>multi-label classification</td> <td>2<br>14<br>5</td> </tr> <tr> <td>Mammography (Masses)</td> <td>mammo_mass</td> <td>CC BY-SA 4.0</td> <td>Mammography</td> <td>pathology<br>mass shape<br>mass margins</td> <td>binary classification<br>multi-label classification<br>multi-label classification</td> <td>2<br>8<br>5</td> </tr> <tr> <td>OCT</td> <td>oct</td> <td>CC BY-SA 4.0</td> <td>OCT</td> <td>disease class<br>urgent referral</td> <td>multi-class classification<br>binary classification</td> <td>4<br>2</td> </tr> <tr> <td>Axial Organ Slices</td> <td>organs_axial</td> <td>CC BY-NC-SA 4.0</td> <td>Abdominal CT</td> <td>organ label</td> <td>multi-class classification</td> <td>11</td> </tr> <tr> <td>Coronal Organ Slices</td> <td>organs_coronal</td> <td>CC BY-NC-SA 4.0</td> <td>Abdominal CT</td> <td>organ label</td> <td>multi-class classification</td> <td>11</td> </tr> <tr> <td>Sagittal Organ Slices</td> <td>organs_sagittal</td> <td>CC BY-NC-SA 4.0</td> <td>Abdominal CT</td> <td>organ label</td> <td>multi-class classification</td> <td>11</td> </tr> <tr> <td>Peripheral Blood Cells</td> <td>pbc</td> <td>CC BY-SA 4.0</td> <td>Microscopy</td> <td>cell class</td> <td>multi-class classification</td> <td>8</td> </tr> <tr> <td>Pediatric Pneumonia</td> <td>pneumonia</td> <td>CC BY-SA 4.0</td> <td>Chest X-ray</td> <td>pneumonia presence<br>disease class</td> <td>binary classification<br>multi-class classification</td> <td>2<br>3</td> </tr> <tr> <td>Skin Lesion Evaluation (Dermoscopy)</td> <td>skinl_derm</td> <td>CC BY-SA 4.0</td> <td>Dermatoscopy</td> <td>Diagnosis<br>Diagnosis grouped<br>Pigment Network<br>Blue Whitish Veil<br>Vascular Structures<br>Vascular Structures grouped<br>Pigmentation<br>Pigmentation grouped<br>Streaks<br>Dots and Globules<br>Regression Structures<br>Regression Structures grouped</td> <td>multi-class classification<br>multi-class classification<br>multi-class classification<br>binary classification<br>multi-class classification<br>multi-class classification<br>multi-class classification<br>multi-class classification<br>multi-class classification<br>multi-class classification<br>multi-class classification<br>binary classification</td> <td>15<br>5<br>3<br>2<br>8<br>3<br>5<br>3<br>3<br>3<br>4<br>2</td> </tr> <tr> <td>Skin Lesion Evaluation (Clinical Photography)</td> <td>skinl_photo</td> <td>CC BY-SA 4.0</td> <td>Clinical skin imaging</td> <td>Diagnosis<br>Diagnosis grouped<br>Pigment Network<br>Blue Whitish Veil<br>Vascular Structures<br>Vascular Structures grouped<br>Pigmentation<br>Pigmentation grouped<br>Streaks<br>Dots and Globules<br>Regression Structures<br>Regression Structures grouped</td> <td>multi-class classification<br>multi-class classification<br>multi-class classification<br>binary classification<br>multi-class classification<br>multi-class classification<br>multi-class classification<br>multi-class classification<br>multi-class classification<br>multi-class classification<br>multi-class classification<br>binary classification</td> <td>15<br>5<br>3<br>2<br>8<br>3<br>5<br>3<br>3<br>3<br>4<br>2</td> </tr> </tbody> </table>
REal-time Data Monitoring for Shared Adaptive, Multi-domain and Personalised Prediction and Decision Making for Long-term Pulmonary Care Ecosystems (RE-SAMPLE)
ClinicalTrials.gov study NCT04955080. IPD Sharing: Not stated. Countries: 1. Publications: 0.
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.