Data Management and Analysis Core

DMAC Summary The Data Management and Analysis Core (DMAC) will ensure wide accessibility of the complex and integrated health and earth science data generated within the Columbia University Northern Plains Superfund Research Program (CUNP-SRP). These efforts will be guided by an overarching mission to treat and share data according to the principles of tribal data sovereignty and the research code set in place by our partnering communities in the Northern Plains. The DMAC will dedicate significant resources to supporting application of existing analysis methods, developing innovative analysis techniques, and ensuring long-term reproducibility of results by leveraging statistical and data science expertise. The DMAC is centrally positioned in the CUNP-SRP and will serve all Projects and Cores, including the Community Engagement Core (CEC) and Research Experience and Training Coordination Core (RETCC), through three aims. Aim 1 will integrate and enhance data management, sharing, and interoperability. We will use established capabilities of the Data Management Unit at Columbia University to develop customized data management and quality assurance plans for each Project/Core, manage data collection and databases, coordinate and harmonize datasets, and provide for their efficient querying. The DMAC will create streamlined data communication across Projects and with external partners and data requestors, following appropriate procedures approved by our partnering tribal communities, by creating an integrated webpage that provides central access to the databases and offers advanced search capabilities. The webpage will act as a platform to locate, access, and mine data while meeting the data sharing requirements of each study. We will also share data via this Database Directory and will work with investigators, data owners, and governmental or policymaking agencies to locate additional available online data resources. Aim 2 will expand statistical resources, data analysis capability, and reproducibility tools. DMAC will provide expertise in established methods for data analysis including statistical and physical modeling. It will also support development of innovative methods, particularly in complex and high-dimensional data inherent to omics research and to complex environmental and geospatial research. Additionally, DMAC will develop, test, and apply robust implementations of new methods for complex data and ensure long-term reproducibility of findings through containerized analysis pipelines. Aim 3 will educate investigators, trainees, and citizen scientists in data sovereignty, sharing, management and analysis. DMAC will collaborate with the RETCC to organize workshops, seminars, and other educational opportunities. Methods, results, and educational resources will be shared with all stakeholders via CUNP-SRP outreach through the CEC, Admin Core, and including the DMAC webpage. Procedures established by DMAC will strive to meet the needs of all investigators and partnering communities, adding substantial value to our collaborations within the CUNP-SRP, across other SRP centers, and to the wider community.