SCISIPBIO: Distinguishing practices of outstanding productivity and expansion in biomedical research

重要的、可重复的科学研究多次改善了全世界的健康状况，提高生物医学研究的科学生产力是一个经常讨论的话题。然而，关于哪些策略可以产生最有意义的差异，缺乏证据。本研究的目的是确定将快速发展的研究机构和实验室集群与同类同行区分开来的途径和产出。研究最具活力的集群的最佳做法和创业举措，应阐明生物医学研究可以扩大和改进的机制。该项目将汇编来自各种研究绩效数据库的数据，包括资助的奖项、出版物、引用、临床试验、提交给数据存储库、专利和商业化等。它强调在实验室一级使用非出版研究生产力措施，因为这些措施可以更及时并涵盖更广泛的活动。将评估与快速增长相关的各种结果指标的可靠性和有效性。随后，拟议的研究将着眼于驱动因素，快速增长的实验室集群的质量，如劳动力和多样性，背后的杰出成果。增长的一个中心指标将是与同行相比，主要研究人员的规模和能力。在分析性别、种族、民族和专业多样性和包容性的价值时，该项目将不仅关注数字代表，还关注决策参与水平。基于与成功的密切联系，将突出最佳做法，并建议研究机构和实验室加以考虑。关于研究绩效的信息的可用性和粒度的最新发展为研究实验室和集群层面的科学生产力分析创造了前所未有的机会。拟议中的项目将侧重于研究较少的测量单位——研究实验室。它将基于对最佳实践案例（公理优越的结果）和控制（其他所有人）的新颖描述。该项目将整合新的数据来源和科学生产力措施，并广泛使用更新的、非出版的研究生产力措施。它将综合最新的、真实世界的证据，并利用来自多个来源的有关正在进行的项目的信息。拟议项目的结果应使人们更好地了解各种结果之间的关系以及重要品质在促进可重复和后续生物医学研究取得更大进展方面的作用。

Consequential, reproducible scientific research has led to better health worldwide many times and increasing scientific productivity of biomedical research is a frequent topic of discussions. However, there is a shortage of evidence on what strategies can make the most meaningful difference. The objective of this research is to identify pathways and outputs that distinguish fast-growing research institutions and clusters of laboratories from their comparable peers. Studying best practices and entrepreneurial initiatives of the most dynamic clusters should elucidate the mechanisms by which biomedical research can expand and improve. This project will compile data from a variety of research performance databases, including funded awards, publications, citations, clinical trials, submissions to data repositories, patenting, and commercialization, and many others. It puts emphasis on the use of non-publication research productivity measures at the laboratory level as they can be more up to date and cover broader range of activities. A variety of outcome metrics will be assessed for reliability and validity in association with fast growth. Subsequently, the proposed study will look into the driving factors, qualities of fast-growing clusters of laboratories, like workforce and diversity, behind outstanding outcomes. A central indicator of growth will be the size and competence of the cohort of principal investigators in comparison to their peers. In analyzing the value of gender, racial, ethnic and professional diversity and inclusiveness, this project will be focused not only on numeric representations but also on the level of participation in decision-making. Based on strong associations with success, best practices will be highlighted and recommended for consideration by research institutions and laboratories. The latest developments in the availability and granularity of information about research performance create unprecedented opportunities for analysis of scientific productivity at the level of research laboratories and clusters. The proposed project will focus on a less well-studied unit of measurement, the research laboratory. It will be based on a novel delineation of best practice cases (axiomatic superior outcomes) and controls (everyone else). The project will integrate new data sources and measures of scientific productivity and extensively use newer, non-publication research productivity measures. It will synthesize up-to-date, real-world evidence and leverage information about ongoing projects from multiple sources. Results of the proposed project should lead to better understanding of the relationships among various outcomes and the role of important qualities in promoting greater progress in reproducible and consequential biomedical research.