Biosciences and Biotechnology Division, Lawrence Livermore National Laboratory, Livermore, California, 94550, USA.
Computing Directorate, Lawrence Livermore National Laboratory, Livermore, California, 94550, USA.
F1000Res. 2023 Nov 1;12:1430. doi: 10.12688/f1000research.140735.1. eCollection 2023.
Ensuring the validity of results from funded programs is a critical concern for agencies that sponsor biological research. In recent years, the open science movement has sought to promote reproducibility by encouraging sharing not only of finished manuscripts but also of data and code supporting their findings. While these innovations have lent support to third-party efforts to replicate calculations underlying key results in the scientific literature, fields of inquiry where privacy considerations or other sensitivities preclude the broad distribution of raw data or analysis may require a more targeted approach to promote the quality of research output. We describe efforts oriented toward this goal that were implemented in one human performance research program, Measuring Biological Aptitude, organized by the Defense Advanced Research Project Agency's Biological Technologies Office. Our team implemented a four-pronged independent verification and validation (IV&V) strategy including 1) a centralized data storage and exchange platform, 2) quality assurance and quality control (QA/QC) of data collection, 3) test and evaluation of performer models, and 4) an archival software and data repository. Our IV&V plan was carried out with assistance from both the funding agency and participating teams of researchers. QA/QC of data acquisition aided in process improvement and the flagging of experimental errors. Holdout validation set tests provided an independent gauge of model performance. In circumstances that do not support a fully open approach to scientific criticism, standing up independent teams to cross-check and validate the results generated by primary investigators can be an important tool to promote reproducibility of results.
确保资助项目的结果有效是赞助生物研究的机构的一个关键关注点。近年来,开放科学运动通过鼓励不仅共享完成的手稿,而且共享支持其发现的数据和代码,以促进可重复性。虽然这些创新为第三方努力复制科学文献中关键结果背后的计算提供了支持,但在隐私考虑或其他敏感性禁止广泛分发原始数据或分析的研究领域,可能需要采取更有针对性的方法来提高研究成果的质量。我们描述了为实现这一目标而在一个人类绩效研究计划中实施的努力,该计划由国防高级研究计划局的生物技术办公室组织,名为测量生物能力。我们的团队实施了四项独立的验证和验证 (IV&V) 策略,包括 1) 集中的数据存储和交换平台,2) 数据收集的质量保证和质量控制 (QA/QC),3) 表演者模型的测试和评估,以及 4) 档案软件和数据存储库。我们的 IV&V 计划在资助机构和参与研究团队的协助下进行。数据采集的 QA/QC 有助于改进流程并标记实验错误。保留验证集测试提供了模型性能的独立衡量标准。在不支持科学批评完全开放方法的情况下,为主要调查员生成的结果建立独立团队进行交叉检查和验证可以成为促进结果可重复性的重要工具。