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   https://doi.org/10.1002/lom3.10168  

   Kearney, William S.; Mariotti, Giulio; Deegan, Linda A.; Fagherazzi, Sergio (April 2017, Limnology and Oceanography: Methods)
2. Dataset Relationship Management

   Zachary G. Ives, Yi Zhang (January 2019, Proceedings of Conference on Innovative Database Systems Research (CIDR 19))

   The database community has largely focused on providing improved transaction management and query capabilities over records (and generalizations thereof). Yet such capabilities address only a small part of today’s data science tasks, which are often much more focused on discovery, linking, comparative analysis, and collaboration across holistic datasets and data products. Data scientists frequently point to a strong need for data management — with respect to their many datasets and data products. We propose the development of the dataset relationship management system to support five main classes of operations on datasets: reuse of schema, data, curation, and work across many datasets; revelation of provenance, context, and assumptions; rapid revision of data and processing steps; system-assisted retargeting of computation to alternative execution environments; and metrics to reward individuals’ contributions to the broader data ecosystem. We argue that the recent adoption of computational notebooks (particularly JupyterLab and Jupyter Notebook), as a unified interface over data tools, provides an ideal way of gathering detailed information about how data is being used, i.e., of transparently capturing dataset provenance and relationships, and thus such notebooks provide an attractive mechanism for integrating dataset relationship management into the data science ecosystem. We briefly outline our experiences in building towards JuNEAU, the first prototype DRMS. 

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3. Efficient Federated Kinship Relationship Identification.

   Wang X; Dervishi L; Li W; Jiang X; Ayday E; Vaidya J (January 2023, AMIA Jt Summits Transl Sci Proc 2023)

   Kinship relationship estimation plays a significant role in today's genome studies. Since genetic data are mostly stored and protected in different silos, retrieving the desirable kinship relationships across federated data warehouses is a non-trivial problem. The ability to identify and connect related individuals is important for both research and clinical applications. In this work, we propose a new privacy-preserving kinship relationship estimation framework: Incremental Update Kinship Identification (INK). The proposed framework includes three key components that allow us to control the balance between privacy and accuracy (of kinship estimation): an incremental process coupled with the use of auxiliary information and informative scores. Our empirical evaluation shows that INK can achieve higher kinship identification correctness while exposing fewer genetic markers. 

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4. Efficient Federated Kinship Relationship Identification

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5. Efficient Federated Kinship Relationship Identification

   Wang X.; Dervishi L; Li W.; Jiang X.; Ayday E.; Vaidya J. (January 2023, American Medical Informatics Association (AMIA) Annual Symposium)