The Midwest Data Hub was established by the National Science Foundation (NSF) in 2015 to build a regional consortium for big data cross-sector partnerships with the goal of managing the end-to-end lifecycle of big data assets, including such issues as ownership, legality, and interoperability. Steps in the data lifecycle include ingestion, validation, curation, quality assessment, anonymization, publication, active data management, and analysis (including information extraction, visualization, and annotation). Automated (or semiautomated) techniques are needed in to keep up with the rapid data rates, large volumes, and immense heterogeneity of big data. Automation may also aid the reproducibility of data processing and analysis workflows. The data challenges and lessons learned by the Midwest Big Data Hub’s thematic communities on such automation efforts are expected to be shared with stakeholders as well as more broadly across the network of NSF Big Data Hubs and other thematic communities.
The following areas are the Hub Communities, (formerly named Spokes) of the Midwest Big Data Hub (http://midwestbigdatahub.org/). These Communities represent areas of focus within the broader big data umbrella.
Communities of the Midwest Big Data Hub
- Digital Agriculture (Agriculture)
- Business Analytics
- Community Resilience
- Health & Biomedicine
- Materials & Manufacturing
- Urban Science and Smart Cities