Scientific documents metadata as a component of the system of the “open science” information resources
Abstract
Open science is a movement that aims to make research results more accessible, including code, data, and scientific papers. It covers many different but often related aspects affecting the entire research life cycle, including open access to publications, open data of research, open source software, open workflows, public science, open educational resources and alternative methods of the research evaluation, including open peer review, expert reviews, etc. The key to effective application and integration of open science resources is their structured description based on the principles of completeness and necessity of meta-information, ease of use and interoperability. The units of such description are metadata. In fact, the quality of open resources begins with the quality of its metadata.
This study does not cover the entire wide spectrum of open science resources. Its purposes are to define the system of characteristics that describe general and specific features of various types of scientific documents as a significant part of scientific knowledge of Open Science.
To achieve the goal of the research it is defined a taxonomy of resources of open scientific documents and proposes an integrated system of their metadata. Proposed system of metadata is based on several classifier sets. It includes three major groups: internal characteristics – description of explicit features of the object of open knowledge (for example, the size or the type of the file), administrative characteristics – information about the object (authors, executers, etc.) and descriptive characteristics - information about the object’s content, its special features, links to other objects related to this.
The metadata system is built based on the analysis of the existing metadata schemas and standards, search engines and digital libraries, and it takes into account similarity and specificity of each type of open documents.
Prombles in programming 2023; 4: 27-38
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DOI: https://doi.org/10.15407/pp2023.04.027
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