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

随之而来的、可重复的科学研究多次改善了全球的健康状况，提高生物医学研究的科学生产率是一个经常讨论的话题。然而，对于哪些策略可以产生最有意义的影响，目前还缺乏证据。这项研究的目的是确定将快速增长的研究机构和实验室集群与其可比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倡议应该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、引文、临床试验、提交给数据存储库、专利申请和商业化，以及许多其他数据。它强调在实验室一级使用非发表的研究生产率衡量标准，因为这些衡量标准更新，涵盖的活动范围更广。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，拟议的研究将探讨杰出成果背后的驱动因素，即快速增长的实验室集群的质量，如劳动力和多样性。增长的一个核心指标将是主要调查员队伍的规模和能力与其同行相比。在分析性别、种族、族裔和专业多样性和包容性的价值时，该项目不仅将侧重于数字表示，而且将侧重于参与决策的程度。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。关于研究业绩的信息的可获得性和粒度方面的最新发展为在研究实验室和集群一级分析科学生产力创造了前所未有的机会。拟议的项目将把重点放在一个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(不言而喻的最优结果)和控制(其他所有人)的新描述。该项目将整合新的数据来源和科学生产力的衡量标准，并广泛使用更新的、非出版的研究生产力衡量标准。它将综合最新的、真实的证据，并利用来自多个来源的正在进行的项目的信息。拟议项目的结果应该有助于更好地理解各种结果之间的关系，以及重要质量在促进可重复和可产生结果的生物医学研究方面取得更大进展的作用。

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.