Removing Barriers to Better Data Practices through Capacity, Opportunity, and Motivation

通过能力、机会和动力消除更好数据实践的障碍

• 批准号: 10710223
• 负责人: Andrew W Brown
• 金额: $ 6.01万
• 依托单位: ARKANSAS CHILDREN'S HOSPITAL RES INST
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10710223
• 关键词: Adherence; Adopted; Awareness; Behavior; Behavioral; Biomedical Research; Biometry; Case Study; Chronic Disease; Code; Communication; Communities; Complement; Conscious; Data; Data Science; Data Set; Discipline; Documentation; Education; Educational Materials; Ensure; Evaluation; FAIR principles; Faculty; Failure; Feedback; Funding; Generations; Goals; Health; Heterogeneity; Institution; Intention; Investments; Learning; Literature; Measurement; Methodology; Methods; Mind; Modality; Modeling; Motivation; National Institute of General Medical Sciences; Obesity; Outcome; Paper; Participant; Privatization; Process; Publications; Reporting; Reproducibility; Research; Research Design; Research Methodology; Research Personnel; Research Project Grants; Research Training; Resource Sharing; Science; Scientist; Self Assessment; Series; Standardization; Structure; System; Theory of Change; Thumb structure; Training; Training and Education; Trust; United States National Institutes of Health; Variant; Work; base; behavior change; data format; data management; data sharing; design; education research; fabrication; flexibility; frontier; high standard; human subject; ideation; improved; insight; instructor; learning materials; lectures; mathematical sciences; pre-clinical; programs; success; trustworthiness; usability; voucher; working group

PROJECT SUMMARY/ABSTRACT Transparent research is important to ensure scientific reports are accurate, to instill trust in the scientific process, and to increase the amount of scientific insight derived from a given research investment. Two important steps for transparent, trustworthy research is for scientists to share their data and their code, and to have another set of analysts verify their results. Many efforts to change investigator behavior around data and code sharing discuss complete workflows, best practices with standardized variables across the discipline, and high standards for FAIR principles. Unfortunately, much biomedical research in general, and preclinical or exploratory research in particular, is idiosyncratic. New outcomes are formed, new methods are created, and community standards may not exist. Furthermore, some researchers learn of data and code sharing after a research project has started, during a paper submission process, or only when someone asks for their data and code. Where possible, our educational materials will focus on best practices. However, given the heterogeneity of types and stages of research needing data and code to be shared, our proposed educational materials will also focus on encouraging better practices to avoid ‘making the perfect the enemy of the good.’ Educational materials will focus on making data and code publically available in a usable and interpretable format, even if not perfectly standardized or FAIR. Simple workflows will be devised for researchers to independently verify their own data and analyses to reduce errors in the literature. Because investigators need to engage with data and code verification at various stages of research, we will present the educational materials in three flows. 1) A complete start-to-finish workflow of data and code verification and sharing. 2) Special use cases, such as preparing data and code from a completed study, sharing code even when data are unable to be shared, and responding to data and code sharing requests after publication. 3) Discrete educational materials, including the lectures, case-studies, self-assessments, readings, and more, to be used outside of the module context as investigators may need. Each piece of educational material will be built upon behavior-change theory, namely the capability, opportunity, and motivation model of behavior change (COM- B). We will structure learning materials with the intention of changing investigator behavior by explicitly connecting materials to build investigator capability, outlining the many opportunities investigators have to verify and share their data and code, and enhancing motivation through positive and negative real-world case studies. Materials will be made in compliance with Sharable Content Object Reference Model (SCORM) standards for easy re-use and incorporation by other educators into their courses and trainings, and comply with accessibility practices. The audience will include investigators at all stages, with preliminary users being drawn from our existing NIH-funded educational programs in basic quantitative sciences and in behavioral chronic disease research, to enable a broad base of scientists to better share and verify code and data.

项目概要/摘要 透明的研究对于确保科学报告的准确性、灌输对科学的信任非常重要 过程，并增加从特定研究投资中获得的科学见解的数量 两个。 透明、值得信赖的研究的重要步骤是科学家分享他们的数据和代码，并 让另一组分析师验证他们的结果，以改变调查人员围绕数据和行为的行为。 代码共享讨论完整的工作流程、跨学科标准化变量的最佳实践，以及 不幸的是，许多生物医学研究，以及临床前或临床前研究都遵循高标准。 尤其是探索性研究，它会形成新的成果、创造新的方法，并且 此外，一些研究人员在经过一段时间的研究后才了解到数据和代码共享。 研究项目已经开始，在论文提交过程中，或者只有当有人索要他们的数据时 在可能的情况下，我们的教育材料将重点关注最佳实践。 研究类型和阶段的异质性需要共享数据和代码，我们提出的教育 材料还将重点鼓励更好的实践，以避免“让完美成为优秀的敌人”。 教育材料将侧重于以可用且可解释的方式公开提供数据和代码 格式，即使没有完全标准化或公平，也会为研究人员设计简单的工作流程。 独立验证自己的数据和分析，以减少文献中的错误。 为了在研究的各个阶段进行数据和代码验证，我们将呈现教育性的 三个流程中的材料。 1) 完整的数据和代码验证和共享工作流程。 特殊用例，例如从已完成的研究中准备数据和代码，即使数据 3）离散型 使用的教育材料，包括讲座、案例研究、自我评估、阅读材料等 每一份教育材料都将建立在调查人员可能需要的模块上下文之外。 行为改变理论，即行为改变的能力、机会和动机模型（COM- B) 我们将构建学习材料，以明确改变研究者的行为。 连接材料以建立调查员的能力，概述调查员必须获得的许多机会 验证并分享他们的数据和代码，并通过正面和负面的现实案例增强动力 研究。材料将按照可共享内容对象参考模型（SCORM）制作。 易于其他教育工作者重复使用和纳入其课程和培训的标准，并遵守 受众将包括各个阶段的调查人员，以及初步用户。 取自我们现有的 NIH 资助的基础定量科学和行为教育项目 慢性病研究，使广大科学家能够更好地共享和验证代码和数据。

项目成果

Use of Pre-Post Treatment Values in Place of Between Group Comparisons Invalidates Meta-Analysis Findings. Commentary: Can Millet Consumption Help Manage Hyperlipidemia and Obesity?: A Systematic Review and Meta-Analysis.

• DOI: 10.3389/fnut.2022.921352
• 发表时间: 2022
• 期刊: FRONTIERS IN NUTRITION
• 影响因子: 5
• 作者: Aly, Marwa;Jamshidi-Naeini, Yasaman;Brown, Andrew W.
• 通讯作者: Brown, Andrew W.

Re-analysis of data from a cluster RCT entitled "health literacy and exercise-focused interventions on clinical measurements in Chinese diabetes patients".

• DOI: 10.1016/j.eclinm.2022.101686
• 发表时间: 2022-11
• 期刊: ECLINICALMEDICINE
• 影响因子: 15.1
• 作者: Jamshidi-Naeini, Yasaman;Golzarri-Arroyo, Lilian;Vorland, Colby J.;Brown, Andrew W.;Allison, David B.
• 通讯作者: Allison, David B.