Local data commons: the sleeping beauty in the community of data commons

• Published in: BMC bioinformatics Vol. 23; no. Suppl 12; pp. 1 - 386
• Main Authors: Jeong, Jong Cheol, Hands, Isaac, Kolesar, Jill M, Rao, Mahadev, Davis, Bront, Dobyns, York, Hurt-Mueller, Joseph, Levens, Justin, Gregory, Jenny, Williams, John, Witt, Lisa, Kim, Eun Mi, Burton, Carlee, Elbiheary, Amir A, Chang, Mingguang, Durbin, Eric B
• Format: Journal Article
• Language: English
• Published: London BioMed Central Ltd 23-09-2022; BioMed Central; BMC
• Subjects: Access control; Big Data; Cancer; Clinical data; Clinical trials; Collaboration; Data analysis; Data harmonization; Data integration; Data models; Data retrieval; Data storage; End-to-end model; Gene expression; Genomic data; Genomics; Guidelines; Health aspects; Informatics; Information storage and retrieval; Infrastructure; Local data commons; Medical research; Methodology; Methods; Online databases; Pipeline design; Public data commons; Quality control; United States
• ISSN: 1471-2105; 1471-2105
• DOI: 10.1186/s12859-022-04922-5

Public Data Commons (PDC) have been highlighted in the scientific literature for their capacity to collect and harmonize big data. On the other hand, local data commons (LDC), located within an institution or organization, have been underrepresented in the scientific literature, even though they are a critical part of research infrastructure. Being closest to the sources of data, LDCs provide the ability to collect and maintain the most up-to-date, high-quality data within an organization, closest to the sources of the data. As a data provider, LDCs have many challenges in both collecting and standardizing data, moreover, as a consumer of PDC, they face problems of data harmonization stemming from the monolithic harmonization pipeline designs commonly adapted by many PDCs. Unfortunately, existing guidelines and resources for building and maintaining data commons exclusively focus on PDC and provide very little information on LDC. This article focuses on four important observations. First, there are three different types of LDC service models that are defined based on their roles and requirements. These can be used as guidelines for building new LDC or enhancing the services of existing LDC. Second, the seven core services of LDC are discussed, including cohort identification and facilitation of genomic sequencing, the management of molecular reports and associated infrastructure, quality control, data harmonization, data integration, data sharing, and data access control. Third, instead of commonly developed monolithic systems, we propose a new data sharing method for data harmonization that combines both divide-and-conquer and bottom-up approaches. Finally, an end-to-end LDC implementation is introduced with real-world examples. Although LDCs are an optimal place to identify and address data quality issues, they have traditionally been relegated to the role of passive data provider for much larger PDC. Indeed, many LDCs limit their functions to only conducting routine data storage and transmission tasks due to a lack of information on how to design, develop, and improve their services using limited resources. We hope that this work will be the first small step in raising awareness among the LDCs of their expanded utility and to publicize to a wider audience the importance of LDC.