Centralized project-specific metadata platforms: toolkit provides new perspectives on open data management within multi-institution and multidisciplinary research projects

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.

A Bibliometric Overview of Open Science Research

“…Interoperability regards the integration of two or more datasets into a whole [43] promoting re-usage [44]. Making data FAIR involves meta-data definition and management [45], which is necessary for multidisciplinary research works [46]. FAIR principles are considered in data infrastructures [47], enhancing data quality [48] and teaching FAIR principles has been materialised by integrating FAIR principles in curricula [49].…”

Factors Influencing Open Science Participation Through Research Data Sharing and Reuse Among Researchers: A Systematic Literature Review

“…Metadata and data standards to ensure the efficient reuse of shared data. These policies mostly prescribe standards for metadata, the descriptive information about datasets to aid in understanding, searching, and utilising the shared datasets [41], and the infrastructure or repository guidelines, which are also set of standards for the archival and accessibility of shared datasets, specifying which repositories to use or how long data should be retained [42].…”

Factors Influencing Open Science Participation Through Research Data Sharing and Reuse Among Researchers: a Systematic Literature Review

“…Metadata and data standards to ensure the efficient reuse of shared data. These policies mostly prescribe standards for metadata, the descriptive information about datasets to aid in understanding, searching, and utilizing the shared datasets (Child et al, 2022), and the infrastructure or repository guidelines, which are also set of standards for the archival and accessibility of shared datasets, specifying which repositories to use or how long data should be retained (Zhu, 2020).…”