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

点击翻译按钮获取中文摘要

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.