An Optimized Generic Client Service Api For Managing Large Datasets Within A Data Repository

Exponential growth in scientific research data demands novel measures for managing the extremely large datasets. In particular, due to advancements in high-resolution microscopy, the nanoscopy scientific research community is producing datasets up to the range of multiple TeraBytes (TB). Systematically acquired datasets of biological specimens are composed of multiple high-resolution images, in the range of 150-200 TB. The management of these extremely large datasets requires an optimized Generic Client Service (GCS) API with an integration into a data repository system. The novel API proposed in this paper provides an abstract interface that connects various disparate systems. The API is optimized to provide an efficient and automated ingest, download of the data and management of its metadata. The ingest and download processes are based on well-defined workflows stated in this paper. The base metadata model for comprehensive description of the datasets is also stated in the paper. The API is seamlessly integrated with a digital data repository system, namely KIT Data Manager to make it adaptable for a wide range of communities. Finally, a simple and easy to use command line tool is realized based on GCS API to manage large datasets of nanoscopy research community.