交付NIH数据共享要求:避免仅在外观上开放数据。

IF 4.1 Q1 HEALTH CARE SCIENCES & SERVICES
Hope Watson, Jack Gallifant, Yuan Lai, Alexander P Radunsky, Cleva Villanueva, Nicole Martinez, Judy Gichoya, Uyen Kim Huynh, Leo Anthony Celi
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引用次数: 0

摘要

今年1月,美国国立卫生研究院(NIH)实施了一项数据管理和共享政策,旨在利用在NIH资助的研究期间收集的数据。COVID-19大流行表明,这种做法对于扩大患者研究同样至关重要。此外,数据共享是防止引入分析偏差的必要保障。虽然大流行提供了一个机会,通过数据共享来限制关键的研究问题,如可重复性和有效性,但这在实践中没有实现,并成为“仅限外观开放数据”(ODIAO)的一个例子。在这里,我们将ODIAO定义为没有发生实际数据共享(例如,材料或数字数据传输)的数据共享意图。提出一个框架,说明与数据共享相关的主要风险,系统地提出风险缓解战略,并从医疗保健角度提供实例。该框架由开放数据研究所和NIH的2023数据管理和共享政策计划指南的关键方面提供信息。通过我们对法律、技术、声誉和商业类别的审查,我们发现了数据共享的障碍,从对通用数据隐私规则的误解到缺乏能够执行大数据传输的技术人员。由此,我们推断,在许多接触点上,数据共享目前过于缺乏动力,无法成为常态。为了向开放数据迈进,我们建议建立激励机制,首先将数据共享重新集中在患者利益上,在拨款要求和委员会中增加条款,以鼓励遵守数据报告惯例。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Delivering on NIH data sharing requirements: avoiding Open Data in Appearance Only.

Delivering on NIH data sharing requirements: avoiding Open Data in Appearance Only.

Delivering on NIH data sharing requirements: avoiding Open Data in Appearance Only.

Delivering on NIH data sharing requirements: avoiding Open Data in Appearance Only.

Introduction In January, the National Institutes of Health (NIH) implemented a Data Management and Sharing Policy aiming to leverage data collected during NIH-funded research. The COVID-19 pandemic illustrated that this practice is equally vital for augmenting patient research. In addition, data sharing acts as a necessary safeguard against the introduction of analytical biases. While the pandemic provided an opportunity to curtail critical research issues such as reproducibility and validity through data sharing, this did not materialise in practice and became an example of 'Open Data in Appearance Only' (ODIAO). Here, we define ODIAO as the intent of data sharing without the occurrence of actual data sharing (eg, material or digital data transfers).Objective Propose a framework that states the main risks associated with data sharing, systematically present risk mitigation strategies and provide examples through a healthcare lens.Methods This framework was informed by critical aspects of both the Open Data Institute and the NIH's 2023 Data Management and Sharing Policy plan guidelines.Results Through our examination of legal, technical, reputational and commercial categories, we find barriers to data sharing ranging from misinterpretation of General Data Privacy Rule to lack of technical personnel able to execute large data transfers. From this, we deduce that at numerous touchpoints, data sharing is presently too disincentivised to become the norm.Conclusion In order to move towards Open Data, we propose the creation of mechanisms for incentivisation, beginning with recentring data sharing on patient benefits, additional clauses in grant requirements and committees to encourage adherence to data reporting practices.

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来源期刊
CiteScore
6.10
自引率
4.90%
发文量
40
审稿时长
18 weeks
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