2018
Overview
Biological data management is the practice of collecting, organizing, storing, and curating the large and varied data produced by biological research and experiments. It encompasses the systems, standards, and workflows used to handle genomic sequences, experimental measurements, clinical records, and other datasets so they can be reliably accessed, integrated, analyzed, and shared. Effective data management is essential to modern life science, where the scale and complexity of data demand structured databases, metadata, quality control, and reproducible methods to turn raw measurements into usable knowledge. As an application area of big data research, biological data management sits at the intersection of computing, database technology, and the life sciences. Research in this subject area addresses the databases and computing infrastructure that underpin large-scale data work, including how data are stored, processed, and made available for analysis. This page gathers peer-reviewed, open-access research relevant to biological data management and the database, computing, and analytical methods used to handle large biological datasets.
Research published in this journal
1 peer-reviewed article, ranked by relevance. Each links to its DOI.
How this research is being cited
The 1 article above has been cited 2 times in the scholarly literature. Citation data via OpenAlex and Crossref, updated Jun 2026.
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2019 · Advances in intelligent systems and computing
A sample of recent works citing this journal's research on Biological Data Management, linking to each citing work.