Publish-Review-Curate Modelling for Data Paper and Dataset: A Collaborative Approach

IF 2.4 3区 管理学 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE
Youngim Jung, Sungsoo Robert Ahn
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Abstract

Research datasets—capturing natural, societal, or artificial phenomena—are critical in generating new scientific insights, validating research models, and supporting data-intensive discovery. Data papers that describe and contextualise these datasets aim to ensure their findability, accessibility, interoperability, and reusability (FAIR) while providing academic credit to data creators. However, the peer review of data papers and associated datasets presents considerable challenges, requiring reviewers to assess both the syntactic and semantic integrity of the data, metadata quality, and domain-specific scientific relevance. Furthermore, the coordination between journal editors, reviewers, and curators demands substantial effort, often leading to publication delays in the conventional review and then publishing framework. This study proposes a novel Publish-Review-Curate (PRC) model tailored to the synchronised publication and review of data papers and their underlying datasets. Building on preprint and open science practices, the model defines a collaborative, multi-stakeholder workflow involving authors, peer reviewers, data experts, and journal editors. The PRC model integrates open feedback, transparent peer review, and structured curation to improve research data's quality, discoverability, and impact. By articulating conceptual and operational workflows, this study contributes a practical framework for modernising data publishing infrastructures and supporting the co-evaluation of narrative and data artefacts.

Abstract Image

数据论文和数据集的发表-评论-策划建模:一种协作方法
研究数据集——捕捉自然、社会或人工现象——对于产生新的科学见解、验证研究模型和支持数据密集型发现至关重要。描述和描述这些数据集的数据论文旨在确保它们的可查找性、可访问性、互操作性和可重用性(FAIR),同时为数据创建者提供学术信誉。然而,数据论文和相关数据集的同行评议提出了相当大的挑战,要求审稿人评估数据的句法和语义完整性、元数据质量和特定领域的科学相关性。此外,期刊编辑、审稿人和策展人之间的协调需要付出大量的努力,这经常导致传统审稿和出版框架中的出版延迟。本研究提出了一种新颖的出版-评论-策划(PRC)模型,该模型适合于数据论文及其基础数据集的同步出版和审查。该模型建立在预印本和开放科学实践的基础上,定义了一个包括作者、同行审稿人、数据专家和期刊编辑在内的协作、多方利益相关者的工作流程。PRC模式整合了公开反馈、透明同行评议和结构化策展,以提高研究数据的质量、可发现性和影响力。通过阐明概念和操作工作流程,本研究为数据发布基础设施的现代化和支持叙事和数据人工制品的共同评估提供了一个实用框架。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Learned Publishing
Learned Publishing INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
4.40
自引率
17.90%
发文量
72
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