Sold by: Janelia Research Campus
Open data
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Deployed on AWS
High resolution images of subcellular structures.
Overview
High resolution images of subcellular structures.
Features and programs
Open Data Sponsorship Program
This dataset is part of the Open Data Sponsorship Program, an AWS program that covers the cost of storage for publicly available high-value cloud-optimized datasets.
Pricing
This is a publicly available data set. No subscription is required.
Legal
Content disclaimer
Vendors are responsible for their product descriptions and other product content. AWS does not warrant that vendors' product descriptions or other product content are accurate, complete, reliable, current, or error-free.
Delivery details
AWS Data Exchange (ADX)
AWS Data Exchange is a service that helps AWS easily share and manage data entitlements from other organizations at scale.
Open data resources
Available with or without an AWS account.
- How to use
- To access these resources, reference the Amazon Resource Name (ARN) using the AWS Command Line Interface (CLI). Learn more
- Description
- Raw FIB-SEM datasets and derived data.
- Resource type
- S3 bucket
- Amazon Resource Name (ARN)
- arn:aws:s3:::janelia-cosem-datasets
- AWS region
- us-east-1
- AWS CLI access (No AWS account required)
- aws s3 ls --no-sign-request s3://janelia-cosem-datasets/
- Description
- Machine learning models for organelle prediction.
- Resource type
- S3 bucket
- Amazon Resource Name (ARN)
- arn:aws:s3:::janelia-cosem-networks
- AWS region
- us-east-1
- AWS CLI access (No AWS account required)
- aws s3 ls --no-sign-request s3://janelia-cosem-networks/
Resources
Vendor resources
Support
Contact
Managed By
How to cite
Cell Organelle Segmentation in Electron Microscopy (COSEM) on AWS was accessed on DATE from https://registry.opendata.aws/janelia-cosem .
License
CC-BY-4.0
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This bucket contains multiple datasets (as Quilt packages) created by the Allen Institute for Cell Science. The types of data included in this bucket are listed below:
1) Field of view or cropped images of cells
2) Segmentations of structures in the images (e.g., boundaries of cells, DNA, other intracellular structures, etc.)
3) Processed versions of the above images and segmentations
4) Machine learning predictions and labels of the data listed above
5) Models trained on the previously listed data
6) Additional supporting non-image data related to the above listed data types (e.g., gene expression data, whole genome sequencing data, features derived from the images or model predictions, metadata)
7) Simulation, analysis, and visualization data of in silico cell structures, cells, and cell populations
External funding:
The generation of some datasets was supported by the National Human Genome Research Institute of the National Institutes under Award Number UM1HG011593. The cont[...]