BioStudies and the Image Data Resource: Expanding Imaging Datasets, Linkage, Metadata, and Value

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Access to primary research data is vital for the advancement of the scientific enterprise. It facilitates the validation of existing observations and provides the raw materials to build on those observations. In the life sciences, research communities have repeatedly collaborated to build resources that allow public submission and access to particular types of datasets. These include gene sequences, protein structural data, and gene and protein expression profiles. In these cases the community united to standardize the structure of the data and its associated metadata, and to create centralized repositories to facilitate deposition, promote discoverability, and ensure the longevity of the data.Much of the published research in the life sciences carries with it detailed image data. These images are routinely used for quantitative measures of biological processes and structures that form the foundation of many of the results published in peer-reviewed life sciences journals. In almost all cases, however, images are presented in published articles in processed, compressed formats that do not accurately convey the quality and complexity of the original image data. Those original data are housed in thousands of individual labs in hundreds of different file formats. The data are difficult for researchers to share and in practice impossible to publish.The sheer size and complexity of image data sets and even of individual multi-dimensional images makes data submission, handling and publication extremely complex. An image-based genome-wide "high content" screen (HCS) may have over a million images, new "virtual slide" and "light sheet" tissue imaging technologies generate individual images that contain gigapixels of data showing tissues or whole organisms at subcellular resolutions. Many of these datasets-acquired on the latest generation imaging systems-- are valuable resources that contain so much data that their full value can only be achieved if a large community is given the opportunity to view, analyse and re-analyse the data, sometimes in combination with other datasets.Just as genomic and structural biology have already done, the imaging community must address the challenges posed by multidimensional image datasets so scientists, educators, students and the wider public can find, share, and validate the data that underlie published scientific results.This proposal connects a growing community resource called BioStudies with a previous BBSRC-funded project that developed a public Image Data Repository to deliver the next step in public resources for scientific images.