This is the dataset associated with the paper titled "Publicly Available Imaging Datasets for Age-related Macular Degeneration: Evaluation according to the Findable, Accessible, Interoperable, Reproducible (FAIR) Principles". Age-related macular degeneration (AMD), a leading cause of vision loss among older adults, affects more than 200 million people worldwide. In this paper, We evaluated openly available AMD-related datasets containing optical coherence tomography (OCT) data against the FAIR principles. This is an archive of the repository that contains the data related to our evaluation. See this inventory for all related resources, including the paper. This dataset is maintained from https://github.com/fairdataihub/FAIR-AMD-OCT-paper-dataset.

This is the dataset associated with the paper titled "Publicly Available Imaging Datasets for Age-related Macular Degeneration: Evaluation according to the Findable, Accessible, Interoperable, Reproducible (FAIR) Principles". Age-related macular degeneration (AMD), a leading cause of vision loss among older adults, affects more than 200 million people worldwide. In this paper, We evaluated openly available AMD-related datasets containing optical coherence tomography (OCT) data against the FAIR principles. This is an archive of the repository that contains the data related to our evaluation. See this inventory for all related resources, including the paper. This dataset is maintained from https://github.com/fairdataihub/FAIR-AMD-OCT-paper-dataset.

This is the dataset associated with the paper titled "Publicly Available Imaging Datasets for Age-related Macular Degeneration: Evaluation according to the Findable, Accessible, Interoperable, Reproducible (FAIR) Principles". Age-related macular degeneration (AMD), a leading cause of vision loss among older adults, affects more than 200 million people worldwide. In this paper, We evaluated openly available AMD-related datasets containing optical coherence tomography (OCT) data against the FAIR principles. This is an archive of the repository that contains the data related to our evaluation. See this inventory for all related resources, including the paper. This dataset is maintained from https://github.com/fairdataihub/FAIR-AMD-OCT-paper-dataset.

This is the dataset associated with the paper titled "Publicly Available Imaging Datasets for Age-related Macular Degeneration: Evaluation according to the Findable, Accessible, Interoperable, Reproducible (FAIR) Principles". Age-related macular degeneration (AMD), a leading cause of vision loss among older adults, affects more than 200 million people worldwide. In this paper, We evaluated openly available AMD-related datasets containing optical coherence tomography (OCT) data against the FAIR principles. This is an archive of the repository that contains the data related to our evaluation. See this inventory for all related resources, including the paper. This dataset is maintained from https://github.com/fairdataihub/FAIR-AMD-OCT-paper-dataset.

This dataset contains data from 204 participants from the pilot period of the AI-READI project (July 19, 2023 to November 30, 2023). Data from multiple modalities are included. The data in this dataset contain no protected health information (PHI). Information related to the sex and race/ethnicity of the participants as well as medication used has also been removed. A detailed description of the dataset is available in the AI-READI documentation for v1.0.0 of the dataset at https://docs.aireadi.org

This dataset contains data from 1067 participants that was collected between July 19, 2023 and July, 31 2024. Data from multiple modalities are included. The data in this dataset contain no protected health information (PHI). Information related to the sex and race/ethnicity of the participants as well as medication used has also been removed. A detailed description of the dataset is available in the AI-READI documentation for v2.0.0 of the dataset at https://docs.aireadi.org

Data related to our FAIR-BioRS manuscript. More details are available at the associated GitHub repository: https://github.com/FAIR-BioRS/Data.

Data related to our FAIR-BioRS manuscript. More details are available at the associated GitHub repository: https://github.com/FAIR-BioRS/Data.

We are developing a novel wireless device named Fecobionics for mapping colonic and anorectal neuromuscular function. The current dataset contains results from pilot Fecobionics tests in the anorectal of healthy human subjects.

We are developing a novel wireless device named Fecobionics for mapping colonic and anorectal neuromuscular function. The current dataset contains results from the Fecobionics tests in the anorectal of healthy human subjects.