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

获取原始研究数据对科学事业的发展至关重要。它促进了对现有观测结果的验证，并提供了建立在这些观测结果基础上的原始材料。在生命科学领域，研究团体多次合作建立资源，允许公众提交和访问特定类型的数据集。这些数据包括基因序列、蛋白质结构数据以及基因和蛋白质表达谱。在这些情况下，社区联合起来标准化数据及其相关元数据的结构，并创建集中的存储库，以促进存储、提高可发现性，并确保数据的寿命。许多已发表的生命科学研究都带有详细的图像数据。这些图像通常用于生物过程和结构的定量测量，形成了许多发表在同行评审的生命科学期刊上的结果的基础。然而，在几乎所有情况下，发表的文章中以经过处理的压缩格式呈现的图像不能准确地传达原始图像数据的质量和复杂性。这些原始数据以数百种不同的文件格式存放在数千个独立的实验室中。研究人员很难分享这些数据，实际上也不可能发表。图像数据集甚至单个多维图像的庞大规模和复杂性使得数据提交、处理和发布极其复杂。基于图像的全基因组“高含量”屏幕（HCS）可能有超过一百万张图像，新的“虚拟幻灯片”和“薄片”组织成像技术生成的单个图像包含数十亿像素的数据，以亚细胞分辨率显示组织或整个生物体。其中许多数据集是在最新一代成像系统上获得的，是宝贵的资源，其中包含如此多的数据，只有在大型社区有机会查看、分析和重新分析这些数据（有时与其他数据集结合使用）的情况下，才能充分发挥其价值。正如基因组学和结构生物学已经做到的那样，成像界必须解决多维图像数据集带来的挑战，这样科学家、教育工作者、学生和更广泛的公众才能发现、分享和验证已发表的科学结果背后的数据。该提案将一个名为BioStudies的不断增长的社区资源与之前bbsrc资助的一个项目联系起来，该项目开发了一个公共图像数据存储库，以提供科学图像的公共资源的下一步。

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.