Defining Big Data for Generalist Repositories
Year: 2025
Type: article
Abstract: This white paper defines Big Data and its key characteristics, including volume, variety, velocity, and veracity. It explains that Generalist Repositories (GRs) are suitable for handling Big Data due to their data-agnostic nature and ability to store large, heterogeneous datasets. The document highlights the increasing importance of Big Data in research and the efforts of the NIH-funded Generalist Repository Ecosystem Initiative (GREI) to support its sharing and reuse. The document outlines specific features and challenges related to Big Data in GRs, such as storage size, file number, data updates, access methods, and interoperability with cloud platforms. It also addresses data packaging, metadata creation, moderated access, curation support, retention periods, and associated costs. The document provides links to additional GREI resources for repository comparisons and selection. It acknowledges the sources used to develop the definition of Big Data and the NIH funding support for this initiative.
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
Funder: National Institutes of Health
Awards: The Harvard Dataverse repository: A generalist repository integrated with a Data Commons, THE GENERALIST REPOSITORY ECOSYSTEM INITIATIVE (GREI), Center for Open Science (COS) Proposal for the NIH Generalist Repository Ecosystem Initiative (GREI), Dryad and the Generalist Repository Ecosystem Initiative (GREI), Vivli: A Generalist Repository For Clinical Trials Data, Zenodo and the Generalist Repository Ecosystem Initiative (GREI), Advancing Figshare and the generalist repository landscape to meet research community needs
OpenAlex ID: W6893095908
FWCI:
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