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Title: HDMF: Hierarchical Data Modeling Framework for Modern Science Data Standards
A ubiquitous problem in aggregating data across different experimental and observational data sources is a lack of software infrastructure that enables flexible and extensible standardization of data and metadata. To address this challenge, we developed HDMF, a hierarchical data modeling framework for modern science data standards. With HDMF, we separate the process of data standardization into three main components: (1) data modeling and specification, (2) data I/O and storage, and (3) data interaction and data APIs. To enable standards to support the complex requirements and varying use cases throughout the data life cycle, HDMF provides object mapping infrastructure to insulate and integrate these various components. This approach supports the flexible development of data standards and extensions, optimized storage backends, and data APIs, while allowing the other components of the data standards ecosystem to remain stable. To meet the demands of modern, large-scale science data, HDMF provides advanced data I/O functionality for iterative data write, lazy data load, and parallel I/O. It also supports optimization of data storage via support for chunking, compression, linking, and modular data storage. We demonstrate the application of HDMF in practice to design NWB 2.0, a modern data standard for collaborative science across the neurophysiology community.  more » « less
Award ID(s):
1816577
NSF-PAR ID:
10177970
Author(s) / Creator(s):
; ; ; ; ; ; ;
Date Published:
Journal Name:
2019 IEEE International Conference on Big Data (Big Data)
Page Range / eLocation ID:
165 to 179
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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