PubMed · 11001023
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M Wadman. 2000-09-14. Gore pledges support for bioscience.. https://doi.org/10.1038/35025294
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Modern research techniques have led to exponential growth in the volume and complexity of scientific data. Consequently, managing these volumes securely and efficiently has become a major challenge. While all research domains face these challenges, life science research is particularly affected because current approaches often rely on a large set of different file formats, with metadata stored in separated databases or spreadsheets. This leads to fragmented datasets, orphaned data, and compromised research reproducibility. Traditional solutions also force researchers to choose between security and accessibility, with encrypted files preventing selective access and indexed formats lacking adequate security for sensitive data. These limitations are particularly problematic in large-scale genomic studies where researchers must decompress multi-gigabyte files to access specific regions, creating computational bottlenecks and inefficient network usage when working with cloud-stored datasets. We introduce Pithos, a next-generation file format specifically designed for scientific data management in distributed cloud environments. The format uses content-defined chunking to enable efficient deduplication across distributed storage systems, thereby reducing storage costs and bandwidth requirements. The append-only structure ensures data immutability and allows for incremental updates without compromising content. Benchmark results show that Pithos outperforms existing solutions in read and write performance, with comparable or improved storage efficiency.
Although a vast amount of life sciences data is generated in the form of images, most scientists still store images on extremely diverse and often incompatible storage media, without any type of metadata structure, and thus with no standard facility with which to conduct searches or analyses. Here we present a solution to unlock the value of scientific images. The Global Image Database (GID) is a web-based (http://www.gwer.ch/qv/gid/gid.ht m ) structured central repository for scientific annotated images. The GID was designed to manage images from a wide spectrum of imaging domains ranging from microscopy to automated screening. The annotations in the GID define the source experiment of the images by describing who the authors of the experiment are, when the images were created, the biological origin of the experimental sample and how the sample was processed for visualization. A collection of experimental imaging protocols provides details of the sample preparation, and labeling, or visualization procedures. In addition, the entries in the GID reference these imaging protocols with the probe sequences or antibody names used in labeling experiments. The GID annotations are searchable by field or globally. The query results are first shown as image thumbnail previews, enabling quick browsing prior to original-sized annotated image retrieval. The development of the GID continues, aiming at facilitating the management and exchange of image data in the scientific community, and at creating new query tools for mining image data.
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