| Literature DB >> 35303952 |
Andrew Wright Child1, Jennifer Hinds2, Lucas Sheneman2, Sven Buerki3.
Abstract
Open science and open data within scholarly research programs are growing both in popularity and by requirement from grant funding agencies and journal publishers. A central component of open data management, especially on collaborative, multidisciplinary, and multi-institutional science projects, is documentation of complete and accurate metadata, workflow, and source code in addition to access to raw data and data products to uphold FAIR (Findable, Accessible, Interoperable, Reusable) principles. Although best practice in data/metadata management is to use established internationally accepted metadata schemata, many of these standards are discipline-specific making it difficult to catalog multidisciplinary data and data products in a way that is easily findable and accessible. Consequently, scattered and incompatible metadata records create a barrier to scientific innovation, as researchers are burdened to find and link multidisciplinary datasets. One possible solution to increase data findability, accessibility, interoperability, reproducibility, and integrity within multi-institutional and interdisciplinary projects is a centralized and integrated data management platform. Overall, this type of interoperable framework supports reproducible open science and its dissemination to various stakeholders and the public in a FAIR manner by providing direct access to raw data and linking protocols, metadata and supporting workflow materials.Entities:
Keywords: Data management; Data science; Metadata; Multi-institutional; Multidisciplinary; Open data; Toolkit
Mesh:
Year: 2022 PMID: 35303952 PMCID: PMC8932304 DOI: 10.1186/s13104-022-05996-3
Source DB: PubMed Journal: BMC Res Notes ISSN: 1756-0500
Fig. 1Centralized metadata/data platform design and functionality schematic. Icon Credits: kareemov1000, Nawicon, lastspark, Eucalyp and Andy Miranda from the Noun Project (https://thenounproject.com/)