Reporting studies conducted using Open Access Data (ROAD) guideline statement

S. A. Alryalat, Lna Malkawi, Randa I. Farah
{"title":"Reporting studies conducted using Open Access Data (ROAD) guideline statement","authors":"S. A. Alryalat, Lna Malkawi, Randa I. Farah","doi":"10.59707/hymrunie2175","DOIUrl":null,"url":null,"abstract":"The growing availability of open access data presents numerous opportunities for researchers, but also raises challenges in terms of adequately reporting methods and findings. This article presents the Reporting of Studies Conducted using Open Access Data (ROAD) guidelines: a comprehensive, practical framework developed to standardize and improve the reporting of research using open access data. The guidelines were built upon existing principles for observational studies, tailored specifically to address the context of open data use. Their development involved an extensive review of published open data studies, and input from a diverse panel of experts through a series of consensus meetings. The ROAD guidelines encompass various aspects of study reporting, including specifying the original dataset, articulating study design and setting, detailing participant selection and variables, and acknowledging data providers. By enhancing transparency and reproducibility, these guidelines aim to improve the quality of research reports, ensure accurate interpretation of results , and foster more effective use of open access data in the scientific community. We invite feedback and further refinement from researchers and practitioners to ensure the continued relevance of the ROAD guidelines in the dynamic landscape of open data research.","PeriodicalId":335220,"journal":{"name":"High Yield Medical Reviews","volume":"8 11","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"High Yield Medical Reviews","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.59707/hymrunie2175","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The growing availability of open access data presents numerous opportunities for researchers, but also raises challenges in terms of adequately reporting methods and findings. This article presents the Reporting of Studies Conducted using Open Access Data (ROAD) guidelines: a comprehensive, practical framework developed to standardize and improve the reporting of research using open access data. The guidelines were built upon existing principles for observational studies, tailored specifically to address the context of open data use. Their development involved an extensive review of published open data studies, and input from a diverse panel of experts through a series of consensus meetings. The ROAD guidelines encompass various aspects of study reporting, including specifying the original dataset, articulating study design and setting, detailing participant selection and variables, and acknowledging data providers. By enhancing transparency and reproducibility, these guidelines aim to improve the quality of research reports, ensure accurate interpretation of results , and foster more effective use of open access data in the scientific community. We invite feedback and further refinement from researchers and practitioners to ensure the continued relevance of the ROAD guidelines in the dynamic landscape of open data research.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
报告使用开放式获取数据(ROAD)准则声明进行的研究
越来越多的开放获取数据为研究人员提供了许多机会,但也在充分报告方法和发现方面提出了挑战。本文介绍了使用开放获取数据(ROAD)进行的研究报告指南:一个全面的、实用的框架,用于标准化和改进使用开放获取数据的研究报告。该指南建立在现有的观察性研究原则的基础上,专门针对开放数据使用的背景进行了定制。它们的发展涉及对已发表的开放数据研究的广泛审查,并通过一系列共识会议听取了不同专家小组的意见。ROAD指南涵盖了研究报告的各个方面,包括指定原始数据集,阐明研究设计和设置,详细说明参与者选择和变量,并承认数据提供者。通过提高透明度和可重复性,这些指南旨在提高研究报告的质量,确保对结果的准确解释,并促进科学界更有效地使用开放获取数据。我们邀请研究人员和实践者提供反馈和进一步完善,以确保ROAD指南在开放数据研究的动态环境中继续保持相关性。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Comparative Analysis of the Bibliographic Data Sources Using PubMed, Scopus, Web of Science, and Lens Decoding the Landscape of Cytomegalovirus Research in Liver Transplantation: An In-Depth Analysis From Data to Diagnosis: Narrative Review of Open-Access Mammography Databases for Breast Cancer Detection Ophthalmology Perspective on The Theory of Amyloid Beta Toxicity: Implications on Future Studies Analysis of retracted articles by Jordanian authors
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1